-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathmain.aux
More file actions
825 lines (825 loc) · 67.5 KB
/
Copy pathmain.aux
File metadata and controls
825 lines (825 loc) · 67.5 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
\relax
\providecommand\hyper@newdestlabel[2]{}
\providecommand\HyField@AuxAddToFields[1]{}
\providecommand\HyField@AuxAddToCoFields[2]{}
\citation{long2015fully}
\citation{he2017mask}
\citation{kirillov2019panoptic}
\citation{radford2021learning}
\citation{liang2023ovseg}
\citation{xu2023san}
\citation{xu2023odise}
\citation{xshanOpenvocabularySemanticSegmentation2024,xwangUseUniversalSegment2024,shinhOpenvocabularySemanticSegmentation2024,yxchngAligningVisionLanguageModel2026}
\citation{kirillov2023sam}
\citation{ravi2024sam2}
\citation{hwangXSAMSegmentAnything2026}
\citation{sgxiaoOpenworldsamExtendingSam22026}
\citation{xuPixelalignedLanguageModel2024,zhangLlavagroundingGroundedVisual2024,youFerretReferGround2024,rasheedGlammPixelGrounding2024}
\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{1}{section.1}\protected@file@percent }
\citation{lai2024lisa}
\citation{renPixellmPixelReasoning2024}
\citation{rasheed2024glamm}
\citation{luoConnectingDotsTrainingFree2026,zhuLensLearningSegment2026,zhouReasoningImplicitSelfsupervised2026,guoSeeingBelievingRichContext2026}
\citation{xuVideosegr1ReasoningVideo2026,chen3DDRESDetailed3D2026,libExploringEfficientOpenvocabulary2026,yanMedreasonerReinforcementLearning2026,wangAffordancer1ReinforcementLearning2026}
\citation{hhuangRenovatingNamesOpenvocabulary2024,libExploringEfficientOpenvocabulary2026,guoSeeingBelievingRichContext2026}
\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Number of publications in the curated survey corpus from 2024 to 2026. The stacked bars group papers into grounded segmentation, reasoning segmentation, open-vocabulary segmentation, data and benchmarks, and promptable segmentation.}}{2}{figure.1}\protected@file@percent }
\newlabel{fig-publication-statistics}{{1}{2}{Number of publications in the curated survey corpus from 2024 to 2026. The stacked bars group papers into grounded segmentation, reasoning segmentation, open-vocabulary segmentation, data and benchmarks, and promptable segmentation}{figure.1}{}}
\citation{long2015fully}
\citation{he2017mask}
\citation{kirillov2019panoptic}
\citation{liang2023ovseg}
\citation{xu2023san}
\citation{xu2023odise}
\citation{xshanOpenvocabularySemanticSegmentation2024,xwangUseUniversalSegment2024,shinhOpenvocabularySemanticSegmentation2024,yxchngAligningVisionLanguageModel2026}
\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Overview and taxonomy of the field evolution and technical routes reviewed in this survey. The top row summarizes the transition from closed-set segmentation to open-vocabulary, promptable, unified vision-language, and reasoning segmentation. The middle row groups the core technical routes. The bottom row places representative methods along the temporal development.}}{3}{figure.2}\protected@file@percent }
\newlabel{fig-intro-overview}{{2}{3}{Overview and taxonomy of the field evolution and technical routes reviewed in this survey. The top row summarizes the transition from closed-set segmentation to open-vocabulary, promptable, unified vision-language, and reasoning segmentation. The middle row groups the core technical routes. The bottom row places representative methods along the temporal development}{figure.2}{}}
\@writefile{toc}{\contentsline {section}{\numberline {2}Background}{3}{section.2}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {2.1}Segmentation paradigms from closed-set to reasoning}{3}{subsection.2.1}\protected@file@percent }
\citation{kirillov2023sam}
\citation{ravi2024sam2}
\citation{xhanBoostingSegmentAnything2025,hwangXSAMSegmentAnything2026,sgxiaoOpenworldsamExtendingSam22026}
\citation{xuPixelalignedLanguageModel2024,zhangLlavagroundingGroundedVisual2024,youFerretReferGround2024,rasheedGlammPixelGrounding2024}
\citation{lai2024lisa}
\citation{renPixellmPixelReasoning2024}
\citation{rasheed2024glamm}
\citation{luoConnectingDotsTrainingFree2026,zhuLensLearningSegment2026,zhuPopenPreferencebasedOptimization2025,zhouReasoningImplicitSelfsupervised2026,guoSeeingBelievingRichContext2026}
\citation{radford2021learning}
\citation{xshanOpenvocabularySemanticSegmentation2024,xwangUseUniversalSegment2024,shinhOpenvocabularySemanticSegmentation2024,yxchngAligningVisionLanguageModel2026}
\citation{kirillov2023sam}
\citation{ravi2024sam2}
\citation{xhanBoostingSegmentAnything2025,hwangXSAMSegmentAnything2026,sgxiaoOpenworldsamExtendingSam22026}
\citation{xuPixelalignedLanguageModel2024,zhangLlavagroundingGroundedVisual2024,youFerretReferGround2024,rasheed2024glamm}
\citation{lai2024lisa,renPixellmPixelReasoning2024,zhuLensLearningSegment2026,guoSeeingBelievingRichContext2026}
\@writefile{toc}{\contentsline {subsection}{\numberline {2.2}Development of vision-language foundation models for segmentation}{4}{subsection.2.2}\protected@file@percent }
\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces Illustration of development axes in open-vocabulary and reasoning segmentation. The three arrows summarize how the semantic space, segmentation framework, and downstream task setting evolve from closed-set segmentation toward language-defined vocabularies, unified or composed vision-language systems, and reasoning-intensive scenario grounding.}}{4}{figure.3}\protected@file@percent }
\newlabel{figbackgrounddevelopmentaxes}{{3}{4}{Illustration of development axes in open-vocabulary and reasoning segmentation. The three arrows summarize how the semantic space, segmentation framework, and downstream task setting evolve from closed-set segmentation toward language-defined vocabularies, unified or composed vision-language systems, and reasoning-intensive scenario grounding}{figure.3}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {2.3}Task family and terminology}{4}{subsection.2.3}\protected@file@percent }
\citation{xuVideosegr1ReasoningVideo2026,chen3DDRESDetailed3D2026,libExploringEfficientOpenvocabulary2026,yanMedreasonerReinforcementLearning2026,wangAffordancer1ReinforcementLearning2026}
\citation{liang2023ovseg}
\citation{xu2023san}
\citation{yxchngAligningVisionLanguageModel2026}
\citation{kirillov2023sam}
\citation{ravi2024sam2}
\citation{hwangXSAMSegmentAnything2026}
\citation{lai2024lisa}
\citation{renPixellmPixelReasoning2024}
\citation{rasheed2024glamm}
\citation{xuVideosegr1ReasoningVideo2026}
\citation{xu2023odise}
\citation{xwangUseUniversalSegment2024}
\citation{renPixellmPixelReasoning2024}
\citation{rasheed2024glamm}
\citation{youFerretReferGround2024}
\citation{zhuLensLearningSegment2026}
\citation{zhouReasoningImplicitSelfsupervised2026}
\citation{luoConnectingDotsTrainingFree2026}
\citation{chen3DDRESDetailed3D2026}
\citation{shinhOpenvocabularySemanticSegmentation2024}
\citation{xshanOpenvocabularySemanticSegmentation2024}
\citation{sgxiaoOpenworldsamExtendingSam22026}
\citation{xhanBoostingSegmentAnything2025}
\citation{zhangLlavagroundingGroundedVisual2024}
\citation{zhuPopenPreferencebasedOptimization2025}
\citation{guoSeeingBelievingRichContext2026}
\citation{libExploringEfficientOpenvocabulary2026}
\citation{yanMedreasonerReinforcementLearning2026}
\citation{wangAffordancer1ReinforcementLearning2026}
\citation{radford2021learning,liang2023ovseg,xshanOpenvocabularySemanticSegmentation2024}
\citation{yangExploringCLIPsDense2025,yxchngAligningVisionLanguageModel2026}
\citation{xwangUseUniversalSegment2024,kirillov2023sam,xhanBoostingSegmentAnything2025,lee2025escnet}
\@writefile{toc}{\contentsline {subsection}{\numberline {2.4}Relation to existing surveys}{5}{subsection.2.4}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {3}Foundations}{5}{section.3}\protected@file@percent }
\newlabel{sec:foundations}{{3}{5}{Foundations}{section.3}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {3.1}Visual, textual, and mask representations}{5}{subsection.3.1}\protected@file@percent }
\citation{xlaiLisaReasoningSegmentation2024,renPixellmPixelReasoning2024,rasheedGlammPixelGrounding2024}
\citation{radford2021learning}
\citation{liang2023ovseg,xu2023san,luo2024pnpovss,yangExploringCLIPsDense2025,yxchngAligningVisionLanguageModel2026}
\citation{kirillov2023sam}
\citation{xhanBoostingSegmentAnything2025,hwangXSAMSegmentAnything2026,sgxiaoOpenworldsamExtendingSam22026,shinhOpenvocabularySemanticSegmentation2024}
\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces Taxonomy and section mapping for this survey. The main method sections are organized into open-vocabulary segmentation, promptable and grounded segmentation, reasoning segmentation, and extended scenarios. The bottom row links this taxonomy to the comparison axes and future directions used later in the manuscript.}}{6}{figure.4}\protected@file@percent }
\newlabel{figbackgroundtaxonomy}{{4}{6}{Taxonomy and section mapping for this survey. The main method sections are organized into open-vocabulary segmentation, promptable and grounded segmentation, reasoning segmentation, and extended scenarios. The bottom row links this taxonomy to the comparison axes and future directions used later in the manuscript}{figure.4}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {3.2}Foundation components}{6}{subsection.3.2}\protected@file@percent }
\citation{xuPixelalignedLanguageModel2024,zhangLlavagroundingGroundedVisual2024,youFerretReferGround2024,xiaoOnerefUnifiedOnetower2024,rasheedGlammPixelGrounding2024}
\citation{xlaiLisaReasoningSegmentation2024}
\citation{renPixellmPixelReasoning2024,rasheedGlammPixelGrounding2024,zhangOmgllavaBridgingImagelevel2024}
\citation{luoConnectingDotsTrainingFree2026,huangPixelsLogicPerceptionReasoning2026}
\citation{sikdarPicazoPixelalignedContrastive2025,yangExploringCLIPsDense2025}
\citation{xuPixelalignedLanguageModel2024,zhangLlavagroundingGroundedVisual2024,xiaoOnerefUnifiedOnetower2024,rasheedGlammPixelGrounding2024}
\citation{xu2023san,shinhOpenvocabularySemanticSegmentation2024,libExploringEfficientOpenvocabulary2026}
\@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces Four foundation paradigms used in this survey. Closed-set segmentation fixes the label space; open-vocabulary segmentation introduces text-defined categories; promptable and grounded segmentation combines reusable mask priors with language grounding; reasoning segmentation adds an inference stage before mask generation.}}{7}{figure.5}\protected@file@percent }
\newlabel{fig:foundation_paradigms}{{5}{7}{Four foundation paradigms used in this survey. Closed-set segmentation fixes the label space; open-vocabulary segmentation introduces text-defined categories; promptable and grounded segmentation combines reusable mask priors with language grounding; reasoning segmentation adds an inference stage before mask generation}{figure.5}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {3.3}Learning and adaptation objectives}{7}{subsection.3.3}\protected@file@percent }
\citation{yanTaskPreferenceOptimization2025,zhuLensLearningSegment2026,zhouReasoningImplicitSelfsupervised2026,yanMedreasonerReinforcementLearning2026,wangAffordancer1ReinforcementLearning2026}
\citation{hhuangRenovatingNamesOpenvocabulary2024,libExploringEfficientOpenvocabulary2026}
\citation{youFerretReferGround2024,rasheedGlammPixelGrounding2024,huGroundingSuiteMeasuringComplex2025}
\citation{guoSeeingBelievingRichContext2026,vvatsGuidelineConsistentSegmentationMultiAgent2026,xuVideosegr1ReasoningVideo2026,chen3DDRESDetailed3D2026,yanMedreasonerReinforcementLearning2026,wangAffordancer1ReinforcementLearning2026}
\citation{everingham2015pascal,lin2014microsoft,caesar2018cocostuff,zhou2017scene,cordts2016cityscapes}
\citation{hhuangRenovatingNamesOpenvocabulary2024,noorimTesttimeAdaptationVisionlanguage2026,ybenigmimFlossFreeLunch2025}
\citation{kirillov2023sam}
\citation{xhanBoostingSegmentAnything2025,hwangXSAMSegmentAnything2026,sgxiaoOpenworldsamExtendingSam22026}
\citation{kazemzadeh2014referitgame,yu2016modeling,mao2016generation}
\citation{xuPixelalignedLanguageModel2024,xiaoOnerefUnifiedOnetower2024,chngMaskGroundingReferring2024}
\citation{rasheedGlammPixelGrounding2024,huGroundingSuiteMeasuringComplex2025}
\@writefile{toc}{\contentsline {subsection}{\numberline {3.4}Evaluation setups}{8}{subsection.3.4}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {4}Datasets and Evaluation}{8}{section.4}\protected@file@percent }
\newlabel{sec:datasets-evaluation}{{4}{8}{Datasets and Evaluation}{section.4}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {4.1}Dataset groups}{8}{subsection.4.1}\protected@file@percent }
\citation{xlaiLisaReasoningSegmentation2024,jwangLlmsegBridgingImage2024}
\citation{zhouReasoningImplicitSelfsupervised2026,zhuLensLearningSegment2026,vvatsGuidelineConsistentSegmentationMultiAgent2026,guoSeeingBelievingRichContext2026}
\citation{atharVicasDatasetCombining2025,munasingheVideoglammLargeMultimodal2025,baiOneTokenSeg2024,sunSamaMultiturnReferential2026}
\citation{libExploringEfficientOpenvocabulary2026,yeRISLADBenchmarkModel2026,quenumLisatLanguageinstructedSegmentation2026}
\citation{chen3DDRESDetailed3D2026,huangSurprise3dDatasetSpatial2026}
\citation{yanMedreasonerReinforcementLearning2026,wangAffordancer1ReinforcementLearning2026}
\citation{everingham2015pascal}
\citation{lin2014microsoft}
\citation{caesar2018cocostuff}
\citation{zhou2017scene}
\citation{cordts2016cityscapes}
\citation{hhuangRenovatingNamesOpenvocabulary2024}
\citation{noorimTesttimeAdaptationVisionlanguage2026}
\citation{libExploringEfficientOpenvocabulary2026}
\citation{xxuanReMEDataCentricFramework2025}
\citation{shiyHarnessingVisionFoundation2025}
\citation{kirillov2023sam}
\citation{kirillov2023sam}
\citation{ravi2024sam2}
\citation{hwangXSAMSegmentAnything2026}
\citation{kazemzadeh2014referitgame}
\citation{yu2016modeling}
\citation{mao2016generation}
\citation{xiaGsvaGeneralizedSegmentation2024}
\citation{rasheedGlammPixelGrounding2024}
\citation{huGroundingSuiteMeasuringComplex2025}
\citation{zhangGroundhogGroundingLarge2024}
\citation{yuanOspreyPixelUnderstanding2024}
\citation{xuPixelalignedLanguageModel2024}
\citation{xlaiLisaReasoningSegmentation2024}
\citation{jwangLlmsegBridgingImage2024}
\citation{zhouReasoningImplicitSelfsupervised2026}
\citation{zhuLensLearningSegment2026}
\citation{atharVicasDatasetCombining2025}
\citation{munasingheVideoglammLargeMultimodal2025}
\citation{baiOneTokenSeg2024}
\citation{sunSamaMultiturnReferential2026}
\citation{chen3DDRESDetailed3D2026}
\citation{huangSurprise3dDatasetSpatial2026}
\citation{yeRISLADBenchmarkModel2026}
\citation{quenumLisatLanguageinstructedSegmentation2026}
\citation{yanMedreasonerReinforcementLearning2026}
\citation{wangAffordancer1ReinforcementLearning2026}
\citation{hhuangRenovatingNamesOpenvocabulary2024,ybenigmimFlossFreeLunch2025}
\citation{kirillov2023sam,xhanBoostingSegmentAnything2025,shiyHarnessingVisionFoundation2025}
\citation{rasheedGlammPixelGrounding2024,huGroundingSuiteMeasuringComplex2025,xuPixelalignedLanguageModel2024}
\citation{xlaiLisaReasoningSegmentation2024,yanMedreasonerReinforcementLearning2026,quenumLisatLanguageinstructedSegmentation2026,wangAffordancer1ReinforcementLearning2026}
\citation{lin2014microsoft,kirillov2019panoptic}
\citation{xiaGsvaGeneralizedSegmentation2024,huangPixelsLogicPerceptionReasoning2026}
\citation{atharVicasDatasetCombining2025,munasingheVideoglammLargeMultimodal2025,liangReferdinoReferringVideo2025}
\@writefile{toc}{\contentsline {subsection}{\numberline {4.2}Annotation granularity}{9}{subsection.4.2}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {4.3}Evaluation metrics}{9}{subsection.4.3}\protected@file@percent }
\citation{rasheedGlammPixelGrounding2024,guoSeeingBelievingRichContext2026}
\citation{shiyHarnessingVisionFoundation2025,noorimTesttimeAdaptationVisionlanguage2026,zpengParameterefficientFinetuningHyperspherical2025}
\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Representative datasets and benchmark families used in open-vocabulary, promptable, grounded, and reasoning segmentation. The table emphasizes annotation form and evaluation role rather than leaderboard results.}}{10}{table.1}\protected@file@percent }
\newlabel{tab:dataset-index}{{1}{10}{Representative datasets and benchmark families used in open-vocabulary, promptable, grounded, and reasoning segmentation. The table emphasizes annotation form and evaluation role rather than leaderboard results}{table.1}{}}
\citation{everingham2015pascal}
\citation{caesar2018cocostuff}
\citation{zhou2017scene}
\citation{cordts2016cityscapes}
\citation{hhuangRenovatingNamesOpenvocabulary2024,ybenigmimFlossFreeLunch2025}
\citation{lin2014microsoft}
\citation{kirillov2019panoptic}
\citation{yu2016modeling}
\citation{mao2016generation}
\citation{xiaGsvaGeneralizedSegmentation2024}
\citation{xlaiLisaReasoningSegmentation2024}
\citation{kirillov2023sam}
\citation{ravi2024sam2}
\citation{hwangXSAMSegmentAnything2026}
\citation{atharVicasDatasetCombining2025}
\citation{munasingheVideoglammLargeMultimodal2025}
\citation{liangReferdinoReferringVideo2025}
\citation{baiOneTokenSeg2024}
\citation{rasheedGlammPixelGrounding2024}
\citation{huGroundingSuiteMeasuringComplex2025}
\citation{guoSeeingBelievingRichContext2026}
\citation{shiyHarnessingVisionFoundation2025}
\citation{noorimTesttimeAdaptationVisionlanguage2026}
\citation{zpengParameterefficientFinetuningHyperspherical2025}
\citation{hhuangRenovatingNamesOpenvocabulary2024,ybenigmimFlossFreeLunch2025}
\citation{xshanOpenvocabularySemanticSegmentation2024,shinhOpenvocabularySemanticSegmentation2024,zhouReasoningImplicitSelfsupervised2026,zhuLensLearningSegment2026}
\citation{atharVicasDatasetCombining2025,chen3DDRESDetailed3D2026,yanMedreasonerReinforcementLearning2026,libExploringEfficientOpenvocabulary2026,wangAffordancer1ReinforcementLearning2026}
\citation{liang2023ovseg}
\citation{xu2023san}
\citation{yliMaskadapterDevilMasks2025}
\citation{xu2023odise}
\citation{xwangUseUniversalSegment2024}
\citation{xxuanReMEDataCentricFramework2025}
\citation{luo2024pnpovss}
\citation{zzhaoDPSegDualpromptCost2025}
\citation{joseDinov2MeetsText2025}
\citation{gejCLIPAdaptedRegiontoTextLearning2025}
\citation{maaydinItaclipBoostingTrainingfree2025}
\citation{lsunCliperHierarchicallyImproving2025}
\citation{dzhangCorrclipReconstructingPatch2025}
\citation{shiyHarnessingVisionFoundation2025}
\citation{zpengParameterefficientFinetuningHyperspherical2025}
\citation{rqorbaniSemanticLibraryAdaptation2025}
\citation{noorimTesttimeAdaptationVisionlanguage2026}
\citation{hhuangRenovatingNamesOpenvocabulary2024}
\citation{ybenigmimFlossFreeLunch2025}
\citation{yliuSteppingOutSimilar2025}
\citation{libExploringEfficientOpenvocabulary2026}
\citation{sduttaAerosegHarnessingSam2025}
\citation{huangZoRIDiscriminativeZeroshot2025}
\citation{zhaoOpenvocabularyRGBThermalSemantic2024}
\citation{liang2023ovseg}
\citation{xu2023san}
\citation{zzhaoDPSegDualpromptCost2025}
\citation{joseDinov2MeetsText2025}
\@writefile{toc}{\contentsline {subsection}{\numberline {4.4}Benchmark comparability}{11}{subsection.4.4}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {5}Open-Vocabulary Segmentation}{11}{section.5}\protected@file@percent }
\newlabel{sec:open-vocabulary-segmentation}{{5}{11}{Open-Vocabulary Segmentation}{section.5}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {5.1}CLIP-based dense alignment}{11}{subsection.5.1}\protected@file@percent }
\citation{lsunCliperHierarchicallyImproving2025}
\citation{lsunCliperHierarchicallyImproving2025}
\citation{luo2024pnpovss}
\citation{maaydinItaclipBoostingTrainingfree2025}
\citation{lsunCliperHierarchicallyImproving2025}
\citation{dzhangCorrclipReconstructingPatch2025}
\citation{shiyHarnessingVisionFoundation2025}
\@writefile{lot}{\contentsline {table}{\numberline {2}{\ignorespaces Metric and protocol families for comparing open-vocabulary and reasoning segmentation. The limitation column records what each metric can fail to capture.}}{12}{table.2}\protected@file@percent }
\newlabel{tab:metric-protocols}{{2}{12}{Metric and protocol families for comparing open-vocabulary and reasoning segmentation. The limitation column records what each metric can fail to capture}{table.2}{}}
\@writefile{lot}{\contentsline {table}{\numberline {3}{\ignorespaces Representative open-vocabulary segmentation methods. The table is an index for Section~\ref {sec:open-vocabulary-segmentation}; detailed limitations are discussed in the corresponding subsections.}}{13}{table.3}\protected@file@percent }
\newlabel{tab:open-vocab-methods}{{3}{13}{Representative open-vocabulary segmentation methods. The table is an index for Section~\ref {sec:open-vocabulary-segmentation}; detailed limitations are discussed in the corresponding subsections}{table.3}{}}
\citation{liang2023ovseg}
\citation{yliMaskadapterDevilMasks2025}
\citation{mleeEffectiveSAMCombination2025,xhanBoostingSegmentAnything2025}
\citation{yliMaskadapterDevilMasks2025}
\citation{yliMaskadapterDevilMasks2025}
\citation{xwangUseUniversalSegment2024}
\citation{xxuanReMEDataCentricFramework2025}
\citation{xwangUseUniversalSegment2024}
\citation{ymiaoLanghopsLanguageGrounded2026}
\citation{dengCoconutpancapJointPanoptic2026}
\@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces Training-free spatial repair for CLIP-based open-vocabulary segmentation. Self-self attention based methods replace CLIP's original last-layer attention to improve patch coherence, while VFM-attention based methods borrow attention maps from an external vision foundation model. CLIPer instead fuses early-layer CLIP information to form a coarse text-aligned segmentation map and then applies fine-grained compensation to recover local details~\citep {lsunCliperHierarchicallyImproving2025}.}}{14}{figure.6}\protected@file@percent }
\newlabel{fig:open-vocab-cliper}{{6}{14}{Training-free spatial repair for CLIP-based open-vocabulary segmentation. Self-self attention based methods replace CLIP's original last-layer attention to improve patch coherence, while VFM-attention based methods borrow attention maps from an external vision foundation model. CLIPer instead fuses early-layer CLIP information to form a coarse text-aligned segmentation map and then applies fine-grained compensation to recover local details~\citep {lsunCliperHierarchicallyImproving2025}}{figure.6}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {5.2}Proposal-then-classification pipelines}{14}{subsection.5.2}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {5.3}Fine-grained semantic and part-level segmentation}{14}{subsection.5.3}\protected@file@percent }
\citation{hhuangRenovatingNamesOpenvocabulary2024}
\citation{ybenigmimFlossFreeLunch2025,yliuSteppingOutSimilar2025}
\citation{xwangUseUniversalSegment2024}
\citation{xwangUseUniversalSegment2024}
\citation{xu2023san}
\citation{zpengParameterefficientFinetuningHyperspherical2025,rqorbaniSemanticLibraryAdaptation2025}
\citation{dukangClassDistributioninducedAttention2025,sjinFeaturePurificationMatters2025,qchenTrainingFreeClassPurification2025}
\citation{shinhOpenvocabularySemanticSegmentation2024}
\citation{liuEfficientFoundationModel2025,libExploringEfficientOpenvocabulary2026}
\citation{noorimTesttimeAdaptationVisionlanguage2026}
\@writefile{lof}{\contentsline {figure}{\numberline {7}{\ignorespaces Mask embedding extraction in proposal-then-classification pipelines. Mask cropping feeds cropped masked regions into CLIP, while mask pooling aggregates CLIP features directly inside proposal masks. Mask-Adapter adds an adapter that converts proposal masks and CLIP features into semantic activation maps before pooling, thereby injecting contextual and mask-aware cues into the final mask embedding~\citep {yliMaskadapterDevilMasks2025}.}}{15}{figure.7}\protected@file@percent }
\newlabel{fig:open-vocab-maskadapter}{{7}{15}{Mask embedding extraction in proposal-then-classification pipelines. Mask cropping feeds cropped masked regions into CLIP, while mask pooling aggregates CLIP features directly inside proposal masks. Mask-Adapter adds an adapter that converts proposal masks and CLIP features into semantic activation maps before pooling, thereby injecting contextual and mask-aware cues into the final mask embedding~\citep {yliMaskadapterDevilMasks2025}}{figure.7}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {5.4}Efficiency, adaptation, and distillation}{15}{subsection.5.4}\protected@file@percent }
\@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces Universal segment embeddings for open-vocabulary segmentation. USE first builds segment-text pairs, such as object, part, and context descriptions associated with masks, and then trains a segment embedding head on top of an image encoder. At inference time, the same image features can be reused with different input segments, producing text-aligned segment embeddings for classification, retrieval, or ranking~\citep {xwangUseUniversalSegment2024}.}}{15}{figure.8}\protected@file@percent }
\newlabel{fig:open-vocab-use}{{8}{15}{Universal segment embeddings for open-vocabulary segmentation. USE first builds segment-text pairs, such as object, part, and context descriptions associated with masks, and then trains a segment embedding head on top of an image encoder. At inference time, the same image features can be reused with different input segments, producing text-aligned segment embeddings for classification, retrieval, or ranking~\citep {xwangUseUniversalSegment2024}}{figure.8}{}}
\citation{kirillov2023sam}
\citation{ravi2024sam2}
\citation{huangFocsamDelvingDeeply2024}
\citation{huangInter2FormerDynamicHybrid2025}
\citation{xhanBoostingSegmentAnything2025}
\citation{mleeEffectiveSAMCombination2025}
\citation{sgxiaoOpenworldsamExtendingSam22026}
\citation{hwangXSAMSegmentAnything2026}
\citation{liu2024groundingdino}
\citation{kirillov2023sam}
\citation{ravi2024sam2}
\citation{zhouThinkYouSegment2026}
\citation{huangUnleashingTemporalspatialReasoning2025}
\citation{cuttanoSamwiseInfusingWisdom2025}
\citation{cshangPromptdrivenReferringImage2024}
\citation{daiCurriculumPointPrompting2024}
\citation{chngMaskGroundingReferring2024}
\citation{xiaoOnerefUnifiedOnetower2024}
\citation{caoReferAnySegmentation2025}
\citation{pengGroundingMultimodalLarge2024}
\citation{youFerretReferGround2024}
\citation{yuanOspreyPixelUnderstanding2024}
\citation{xuPixelalignedLanguageModel2024}
\citation{zhangLlavagroundingGroundedVisual2024}
\citation{rasheedGlammPixelGrounding2024}
\citation{huGroundingSuiteMeasuringComplex2025}
\citation{grosiShowTellBenchmark2025}
\citation{tangVisualPositionPrompt2026}
\citation{yangRepurposingSAMEfficient2026}
\citation{wangIterprimeZeroshotReferring2025}
\citation{kirillov2023sam}
\citation{ravi2024sam2}
\citation{ravi2024sam2}
\citation{ravi2024sam2}
\citation{huangFocsamDelvingDeeply2024}
\citation{huangInter2FormerDynamicHybrid2025}
\@writefile{toc}{\contentsline {subsection}{\numberline {5.5}Summary and discussion}{16}{subsection.5.5}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {6}Promptable and Grounded Segmentation}{16}{section.6}\protected@file@percent }
\newlabel{sec:promptable-grounded-segmentation}{{6}{16}{Promptable and Grounded Segmentation}{section.6}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {6.1}SAM and SAM2 as mask priors}{16}{subsection.6.1}\protected@file@percent }
\@writefile{lot}{\contentsline {table}{\numberline {4}{\ignorespaces Representative promptable and grounded segmentation methods. The table follows the same indexing function as Table~\ref {tab:open-vocab-methods}: it records the route, foundation prior, prompt or grounding interface, and the main contribution or limitation rather than serving as a leaderboard.}}{17}{table.4}\protected@file@percent }
\newlabel{tab:promptable-grounded-methods}{{4}{17}{Representative promptable and grounded segmentation methods. The table follows the same indexing function as Table~\ref {tab:open-vocab-methods}: it records the route, foundation prior, prompt or grounding interface, and the main contribution or limitation rather than serving as a leaderboard}{table.4}{}}
\citation{xhanBoostingSegmentAnything2025}
\citation{mleeEffectiveSAMCombination2025}
\citation{sgxiaoOpenworldsamExtendingSam22026}
\citation{hwangXSAMSegmentAnything2026}
\citation{liu2024groundingdino}
\citation{zhouThinkYouSegment2026}
\citation{huangUnleashingTemporalspatialReasoning2025}
\citation{cuttanoSamwiseInfusingWisdom2025,wangSam2loveSegmentAnything2025}
\@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces SAM2 as a promptable mask prior for images and videos. A frame is encoded by the image encoder, combined with previous-frame memories through memory attention, and decoded with user prompts such as masks, points, or boxes. The predicted mask is then encoded into a memory bank, enabling later frames to reuse temporal information for interactive video segmentation~\citep {ravi2024sam2}.}}{18}{figure.9}\protected@file@percent }
\newlabel{fig:promptable-sam2}{{9}{18}{SAM2 as a promptable mask prior for images and videos. A frame is encoded by the image encoder, combined with previous-frame memories through memory attention, and decoded with user prompts such as masks, points, or boxes. The predicted mask is then encoded into a memory bank, enabling later frames to reuse temporal information for interactive video segmentation~\citep {ravi2024sam2}}{figure.9}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {6.2}Grounded-SAM composition}{18}{subsection.6.2}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {6.3}Region-word and mask-text grounding}{18}{subsection.6.3}\protected@file@percent }
\citation{cshangPromptdrivenReferringImage2024}
\citation{daiCurriculumPointPrompting2024}
\citation{chngMaskGroundingReferring2024}
\citation{xiaoOnerefUnifiedOnetower2024}
\citation{liu2024groundingdino}
\citation{liu2024groundingdino}
\citation{xiaoOnerefUnifiedOnetower2024}
\citation{xiaoOnerefUnifiedOnetower2024}
\citation{caoReferAnySegmentation2025}
\citation{wangIterprimeZeroshotReferring2025}
\citation{liuHybridGloballocalRepresentation2025}
\citation{pengGroundingMultimodalLarge2024}
\citation{youFerretReferGround2024}
\citation{xuPixelalignedLanguageModel2024}
\citation{yuanOspreyPixelUnderstanding2024}
\citation{zhangLlavagroundingGroundedVisual2024}
\citation{rasheedGlammPixelGrounding2024}
\citation{huGroundingSuiteMeasuringComplex2025}
\citation{huGroundingSuiteMeasuringComplex2025,guoSeeingBelievingRichContext2026}
\@writefile{lof}{\contentsline {figure}{\numberline {10}{\ignorespaces Grounding DINO as a language-guided localization module for Grounded-SAM pipelines. Text and image backbones first produce modality-specific features, a feature enhancer updates both streams, language-guided query selection initializes cross-modality queries, and the decoder predicts localized outputs under contrastive and localization losses. This architecture explains why Grounded-SAM systems often use Grounding DINO to convert text into boxes before SAM produces precise masks~\citep {liu2024groundingdino}.}}{19}{figure.10}\protected@file@percent }
\newlabel{fig:promptable-groundingdino}{{10}{19}{Grounding DINO as a language-guided localization module for Grounded-SAM pipelines. Text and image backbones first produce modality-specific features, a feature enhancer updates both streams, language-guided query selection initializes cross-modality queries, and the decoder predicts localized outputs under contrastive and localization losses. This architecture explains why Grounded-SAM systems often use Grounding DINO to convert text into boxes before SAM produces precise masks~\citep {liu2024groundingdino}}{figure.10}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {6.4}Pixel-grounded multimodal interfaces}{19}{subsection.6.4}\protected@file@percent }
\citation{grosiShowTellBenchmark2025}
\citation{cshangPromptdrivenReferringImage2024}
\citation{tangVisualPositionPrompt2026}
\citation{yangRepurposingSAMEfficient2026}
\citation{rasheedGlammPixelGrounding2024}
\citation{rasheedGlammPixelGrounding2024}
\@writefile{lof}{\contentsline {figure}{\numberline {11}{\ignorespaces OneRef's one-tower referring model. The image branch applies referring-aware dynamic image masking, while the text branch masks the words related to the referred target. A shared multi-head self-attention encoder feeds referring mask image modeling and referring mask language modeling heads, which reconstruct visual content and language content while scoring target-relation consistency~\citep {xiaoOnerefUnifiedOnetower2024}.}}{20}{figure.11}\protected@file@percent }
\newlabel{fig:promptable-oneref}{{11}{20}{OneRef's one-tower referring model. The image branch applies referring-aware dynamic image masking, while the text branch masks the words related to the referred target. A shared multi-head self-attention encoder feeds referring mask image modeling and referring mask language modeling heads, which reconstruct visual content and language content while scoring target-relation consistency~\citep {xiaoOnerefUnifiedOnetower2024}}{figure.11}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {6.5}Prompt learning and adapter-based transfer}{20}{subsection.6.5}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {6.6}Summary and discussion}{20}{subsection.6.6}\protected@file@percent }
\citation{xlaiLisaReasoningSegmentation2024}
\citation{jwangLlmsegBridgingImage2024}
\citation{xiaGsvaGeneralizedSegmentation2024}
\citation{qianReasoningAttendTry2025}
\citation{renPixellmPixelReasoning2024}
\citation{zhangOmgllavaBridgingImagelevel2024}
\citation{feiVitronUnifiedPixellevel2024}
\citation{liuUnipixelUnifiedObject2026}
\citation{wangAltoAdaptivelengthTokenizer2026}
\citation{zongGroundvTeachingVlms2025}
\citation{weiInstructsegUnifyingInstructed2025}
\citation{jangMmrLargescaleBenchmark2025}
\citation{huGroundingSuiteMeasuringComplex2025}
\citation{jwangLlmsegBridgingImage2024}
\citation{luoConnectingDotsTrainingFree2026}
\citation{huangPixelsLogicPerceptionReasoning2026}
\citation{ningEnhancingSpatialReasoning2025}
\citation{zhuPopenPreferencebasedOptimization2025}
\citation{yanTaskPreferenceOptimization2025}
\citation{zhuLensLearningSegment2026}
\citation{zhouReasoningImplicitSelfsupervised2026}
\citation{huangSamr1LeveragingSam2026}
\citation{wuSeeSaySegment2024}
\citation{xiaGsvaGeneralizedSegmentation2024}
\citation{zhangLlavagroundingGroundedVisual2024}
\citation{rasheedGlammPixelGrounding2024}
\citation{guoSeeingBelievingRichContext2026}
\citation{vvatsGuidelineConsistentSegmentationMultiAgent2026}
\citation{xlaiLisaReasoningSegmentation2024}
\citation{jwangLlmsegBridgingImage2024}
\citation{xiaGsvaGeneralizedSegmentation2024}
\citation{qianReasoningAttendTry2025}
\@writefile{lof}{\contentsline {figure}{\numberline {12}{\ignorespaces GLaMM as a pixel-grounded multimodal conversation interface. Global image features and region features are converted into image-region prompts for the LLM, while grounding image features are passed to a pixel decoder through output prompts. The same interface supports referring expression segmentation, image-level captioning, region-level captioning, phrase grounding, and grounded conversation in which generated object mentions are linked to masks~\citep {rasheedGlammPixelGrounding2024}.}}{21}{figure.12}\protected@file@percent }
\newlabel{fig:promptable-glamm}{{12}{21}{GLaMM as a pixel-grounded multimodal conversation interface. Global image features and region features are converted into image-region prompts for the LLM, while grounding image features are passed to a pixel decoder through output prompts. The same interface supports referring expression segmentation, image-level captioning, region-level captioning, phrase grounding, and grounded conversation in which generated object mentions are linked to masks~\citep {rasheedGlammPixelGrounding2024}}{figure.12}{}}
\@writefile{toc}{\contentsline {section}{\numberline {7}Reasoning Segmentation and Pixel Grounding}{21}{section.7}\protected@file@percent }
\newlabel{sec:reasoning-segmentation}{{7}{21}{Reasoning Segmentation and Pixel Grounding}{section.7}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {7.1}MLLM-guided segmentation}{21}{subsection.7.1}\protected@file@percent }
\citation{renPixellmPixelReasoning2024}
\citation{zhangOmgllavaBridgingImagelevel2024}
\citation{feiVitronUnifiedPixellevel2024}
\citation{liuUnipixelUnifiedObject2026,wangAltoAdaptivelengthTokenizer2026}
\@writefile{lot}{\contentsline {table}{\numberline {5}{\ignorespaces Representative reasoning segmentation and pixel-grounding methods. The table indexes Section~\ref {sec:reasoning-segmentation}; detailed limitations are discussed in the corresponding subsections.}}{22}{table.5}\protected@file@percent }
\newlabel{tab:reasoning-methods}{{5}{22}{Representative reasoning segmentation and pixel-grounding methods. The table indexes Section~\ref {sec:reasoning-segmentation}; detailed limitations are discussed in the corresponding subsections}{table.5}{}}
\citation{xlaiLisaReasoningSegmentation2024}
\citation{xlaiLisaReasoningSegmentation2024}
\citation{jwangLlmsegBridgingImage2024}
\citation{luoConnectingDotsTrainingFree2026}
\citation{huangPixelsLogicPerceptionReasoning2026}
\citation{zongGroundvTeachingVlms2025}
\citation{weiInstructsegUnifyingInstructed2025}
\citation{jangMmrLargescaleBenchmark2025}
\citation{huGroundingSuiteMeasuringComplex2025}
\citation{ningEnhancingSpatialReasoning2025}
\citation{vvatsGuidelineConsistentSegmentationMultiAgent2026}
\citation{zhuLensLearningSegment2026}
\citation{zhuLensLearningSegment2026}
\citation{zhuPopenPreferencebasedOptimization2025}
\citation{zhuLensLearningSegment2026}
\citation{zhouReasoningImplicitSelfsupervised2026}
\citation{huangSamr1LeveragingSam2026}
\@writefile{toc}{\contentsline {subsection}{\numberline {7.2}Reasoning-to-mask generation}{23}{subsection.7.2}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {7.3}RL and preference optimization}{23}{subsection.7.3}\protected@file@percent }
\citation{yanTaskPreferenceOptimization2025}
\citation{shenFinegrainedPreferenceOptimization2026}
\citation{wuSeeSaySegment2024}
\citation{xiaGsvaGeneralizedSegmentation2024}
\@writefile{lof}{\contentsline {figure}{\numberline {13}{\ignorespaces LISA's embedding-as-mask pipeline for reasoning segmentation. This placeholder should be replaced by the original pipeline figure from LISA, which shows the image and text query entering an MLLM, the generated \texttt {<SEG>} token, and the decoding of its hidden embedding into a segmentation mask~\citep {xlaiLisaReasoningSegmentation2024}.}}{24}{figure.13}\protected@file@percent }
\newlabel{fig:reasoning-lisa-placeholder}{{13}{24}{LISA's embedding-as-mask pipeline for reasoning segmentation. This placeholder should be replaced by the original pipeline figure from LISA, which shows the image and text query entering an MLLM, the generated \texttt {<SEG>} token, and the decoding of its hidden embedding into a segmentation mask~\citep {xlaiLisaReasoningSegmentation2024}}{figure.13}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {14}{\ignorespaces LENS RL framework for text-prompted segmentation. This placeholder should be replaced by the original LENS overview figure, which contrasts supervised \texttt {<seg>} prompting with an end-to-end RL framework using context queries, a connector to SAM, and unified rewards over reasoning format, localization, and mask quality~\citep {zhuLensLearningSegment2026}.}}{24}{figure.14}\protected@file@percent }
\newlabel{fig:reasoning-lens-placeholder}{{14}{24}{LENS RL framework for text-prompted segmentation. This placeholder should be replaced by the original LENS overview figure, which contrasts supervised \texttt {<seg>} prompting with an end-to-end RL framework using context queries, a connector to SAM, and unified rewards over reasoning format, localization, and mask quality~\citep {zhuLensLearningSegment2026}}{figure.14}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {7.4}Grounded dialogue and hallucination control}{24}{subsection.7.4}\protected@file@percent }
\citation{zhangLlavagroundingGroundedVisual2024,rasheedGlammPixelGrounding2024}
\citation{huGroundingSuiteMeasuringComplex2025,zongGroundvTeachingVlms2025}
\citation{guoSeeingBelievingRichContext2026}
\citation{vvatsGuidelineConsistentSegmentationMultiAgent2026}
\citation{baiOneTokenSeg2024}
\citation{zhengVillaVideoReasoning2025}
\citation{xuVideosegr1ReasoningVideo2026}
\citation{sunSamaMultiturnReferential2026}
\citation{munasingheVideoglammLargeMultimodal2025}
\citation{atharVicasDatasetCombining2025}
\citation{baiOneTokenSeg2024}
\citation{zhengVillaVideoReasoning2025}
\citation{sunSamaMultiturnReferential2026}
\citation{sunSamaMultiturnReferential2026}
\citation{zhouThinkYouSegment2026}
\citation{huangUnleashingTemporalspatialReasoning2025}
\citation{yingOmnimodalExpressionsReasoning2025}
\citation{zhongOmnir1ReinforcementLearning2026}
\citation{zhouThinkYouSegment2026}
\citation{zhouThinkYouSegment2026}
\citation{yingOmnimodalExpressionsReasoning2025}
\citation{chen3DDRESDetailed3D2026}
\citation{huangSurprise3dDatasetSpatial2026}
\citation{jianglOpenvocabulary3dSemantic2024}
\citation{yyinSai3dSegmentAny2024}
\citation{zwangXmask3dCrossmodalMask2024}
\citation{mzhouOV3DCGOpenvocabulary3D2025}
\citation{chen3DDRESDetailed3D2026}
\citation{huangSurprise3dDatasetSpatial2026}
\citation{libExploringEfficientOpenvocabulary2026}
\citation{sduttaAerosegHarnessingSam2025}
\citation{huangZoRIDiscriminativeZeroshot2025}
\citation{yeRISLADBenchmarkModel2026}
\citation{quenumLisatLanguageinstructedSegmentation2026}
\citation{kuckrejaGeochatGroundedLarge2024}
\citation{libExploringEfficientOpenvocabulary2026}
\citation{yeRISLADBenchmarkModel2026}
\citation{quenumLisatLanguageinstructedSegmentation2026}
\citation{yanMedreasonerReinforcementLearning2026}
\citation{chenMIMOMedicalVision2025}
\citation{yanMedreasonerReinforcementLearning2026}
\citation{qianAffordancellmGroundingAffordance2024}
\citation{wangAffordancer1ReinforcementLearning2026}
\citation{zhangOpenhoiOpenworldHandobject2026}
\citation{zhengInstructionguidedVisualMasking2024}
\citation{wangAffordancer1ReinforcementLearning2026}
\citation{wangAffordancer1ReinforcementLearning2026}
\citation{zhengInstructionguidedVisualMasking2024}
\citation{baiOneTokenSeg2024}
\@writefile{toc}{\contentsline {subsection}{\numberline {7.5}Summary and discussion}{25}{subsection.7.5}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {8}Extended Scenarios}{25}{section.8}\protected@file@percent }
\newlabel{sec:extended-scenarios}{{8}{25}{Extended Scenarios}{section.8}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {8.1}Video reasoning and tracking-aware segmentation}{25}{subsection.8.1}\protected@file@percent }
\citation{zhengVillaVideoReasoning2025}
\citation{xuVideosegr1ReasoningVideo2026}
\citation{atharVicasDatasetCombining2025,munasingheVideoglammLargeMultimodal2025,sunSamaMultiturnReferential2026}
\@writefile{lot}{\contentsline {table}{\numberline {6}{\ignorespaces Representative extended scenarios for open-vocabulary, promptable, grounded, and reasoning segmentation. Dataset and protocol facts are taken from the cited papers rather than inferred from method names; detailed evaluation limitations are discussed in the corresponding subsections.}}{26}{table.6}\protected@file@percent }
\newlabel{tab:extended-scenario-index}{{6}{26}{Representative extended scenarios for open-vocabulary, promptable, grounded, and reasoning segmentation. Dataset and protocol facts are taken from the cited papers rather than inferred from method names; detailed evaluation limitations are discussed in the corresponding subsections}{table.6}{}}
\citation{zhengVillaVideoReasoning2025}
\citation{zhengVillaVideoReasoning2025}
\citation{ravi2024sam2}
\citation{zhouThinkYouSegment2026}
\citation{huangUnleashingTemporalspatialReasoning2025}
\citation{yingOmnimodalExpressionsReasoning2025}
\citation{zhongOmnir1ReinforcementLearning2026}
\citation{chen3DDRESDetailed3D2026}
\citation{chen3DDRESDetailed3D2026}
\citation{chen3DDRESDetailed3D2026}
\citation{huangSurprise3dDatasetSpatial2026}
\citation{ningEnhancingSpatialReasoning2025}
\citation{jianglOpenvocabulary3dSemantic2024}
\citation{yyinSai3dSegmentAny2024}
\citation{zwangXmask3dCrossmodalMask2024}
\citation{mzhouOV3DCGOpenvocabulary3D2025}
\@writefile{toc}{\contentsline {subsection}{\numberline {8.2}Audio-visual and omnimodal grounding}{27}{subsection.8.2}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {8.3}3D and multiview referring segmentation}{27}{subsection.8.3}\protected@file@percent }
\citation{libExploringEfficientOpenvocabulary2026}
\citation{sduttaAerosegHarnessingSam2025}
\citation{huangZoRIDiscriminativeZeroshot2025}
\citation{libExploringEfficientOpenvocabulary2026}
\citation{libExploringEfficientOpenvocabulary2026}
\citation{yeRISLADBenchmarkModel2026}
\citation{quenumLisatLanguageinstructedSegmentation2026}
\citation{kuckrejaGeochatGroundedLarge2024,zhangFineRSFinegrainedReasoning2026}
\@writefile{lof}{\contentsline {figure}{\numberline {15}{\ignorespaces ViLLa's video reasoning segmentation framework. This placeholder should be replaced by the original overall framework figure from ViLLa, which shows the key segment extractor, context synthesizer, and hierarchical temporal synchronizer that connect user intent, selected video segments, and multi-scale segmentation tokens~\citep {zhengVillaVideoReasoning2025}.}}{28}{figure.15}\protected@file@percent }
\newlabel{fig:extended-villa-placeholder}{{15}{28}{ViLLa's video reasoning segmentation framework. This placeholder should be replaced by the original overall framework figure from ViLLa, which shows the key segment extractor, context synthesizer, and hierarchical temporal synchronizer that connect user intent, selected video segments, and multi-scale segmentation tokens~\citep {zhengVillaVideoReasoning2025}}{figure.15}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {16}{\ignorespaces 3D-DRES task formulation and phrase-to-instance grounding. This placeholder should be replaced by the original task illustration comparing 3D-REC, 3D-RES, 3D-GRES, and 3D-DRES~\citep {chen3DDRESDetailed3D2026}.}}{28}{figure.16}\protected@file@percent }
\newlabel{fig:extended-3ddres-placeholder}{{16}{28}{3D-DRES task formulation and phrase-to-instance grounding. This placeholder should be replaced by the original task illustration comparing 3D-REC, 3D-RES, 3D-GRES, and 3D-DRES~\citep {chen3DDRESDetailed3D2026}}{figure.16}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {8.4}Remote sensing and aerial imagery}{28}{subsection.8.4}\protected@file@percent }
\citation{yanMedreasonerReinforcementLearning2026}
\citation{yanMedreasonerReinforcementLearning2026}
\citation{yanMedreasonerReinforcementLearning2026}
\citation{chenMIMOMedicalVision2025}
\citation{qianAffordancellmGroundingAffordance2024}
\citation{wangAffordancer1ReinforcementLearning2026}
\citation{zhangOpenhoiOpenworldHandobject2026}
\citation{zhengInstructionguidedVisualMasking2024}
\@writefile{lof}{\contentsline {figure}{\numberline {17}{\ignorespaces OVRSISBench and RSKT-Seg for remote-sensing open-vocabulary segmentation. This placeholder should be replaced by the original benchmark or framework figure showing the open-vocabulary dataset division, vocabulary overlap, and remote-sensing examples~\citep {libExploringEfficientOpenvocabulary2026}.}}{29}{figure.17}\protected@file@percent }
\newlabel{fig:extended-ovrsis-placeholder}{{17}{29}{OVRSISBench and RSKT-Seg for remote-sensing open-vocabulary segmentation. This placeholder should be replaced by the original benchmark or framework figure showing the open-vocabulary dataset division, vocabulary overlap, and remote-sensing examples~\citep {libExploringEfficientOpenvocabulary2026}}{figure.17}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {8.5}Medical, affordance, and embodied grounding}{29}{subsection.8.5}\protected@file@percent }
\@writefile{lof}{\contentsline {figure}{\numberline {18}{\ignorespaces MedReasoner's clinical reasoning-to-grounding pipeline. This placeholder should be replaced by the original framework figure showing how implicit clinical prompts are transformed into spatial prompts and pixel-level masks with RL rewards~\citep {yanMedreasonerReinforcementLearning2026}.}}{29}{figure.18}\protected@file@percent }
\newlabel{fig:extended-medreasoner-placeholder}{{18}{29}{MedReasoner's clinical reasoning-to-grounding pipeline. This placeholder should be replaced by the original framework figure showing how implicit clinical prompts are transformed into spatial prompts and pixel-level masks with RL rewards~\citep {yanMedreasonerReinforcementLearning2026}}{figure.18}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {8.6}Summary and discussion}{29}{subsection.8.6}\protected@file@percent }
\citation{luo2024pnpovss}
\citation{zzhaoDPSegDualpromptCost2025}
\citation{maaydinItaclipBoostingTrainingfree2025}
\citation{lsunCliperHierarchicallyImproving2025}
\citation{dzhangCorrclipReconstructingPatch2025}
\citation{shiyHarnessingVisionFoundation2025}
\citation{liang2023ovseg}
\citation{xu2023san}
\citation{yliMaskadapterDevilMasks2025}
\citation{xwangUseUniversalSegment2024}
\citation{xxuanReMEDataCentricFramework2025}
\citation{kirillov2023sam}
\citation{ravi2024sam2}
\citation{liu2024groundingdino}
\citation{zhouThinkYouSegment2026}
\citation{huangUnleashingTemporalspatialReasoning2025}
\citation{sgxiaoOpenworldsamExtendingSam22026}
\citation{xiaoOnerefUnifiedOnetower2024}
\citation{chngMaskGroundingReferring2024}
\citation{renPixellmPixelReasoning2024}
\citation{yuanOspreyPixelUnderstanding2024}
\citation{rasheedGlammPixelGrounding2024}
\citation{huGroundingSuiteMeasuringComplex2025}
\citation{xlaiLisaReasoningSegmentation2024}
\citation{jwangLlmsegBridgingImage2024}
\citation{xiaGsvaGeneralizedSegmentation2024}
\citation{renPixellmPixelReasoning2024}
\citation{zhangOmgllavaBridgingImagelevel2024}
\citation{zhuLensLearningSegment2026}
\citation{zhouReasoningImplicitSelfsupervised2026}
\citation{huangSamr1LeveragingSam2026}
\citation{baiOneTokenSeg2024}
\citation{zhengVillaVideoReasoning2025}
\citation{libExploringEfficientOpenvocabulary2026}
\citation{chen3DDRESDetailed3D2026}
\citation{huangSurprise3dDatasetSpatial2026}
\citation{yanMedreasonerReinforcementLearning2026}
\citation{wangAffordancer1ReinforcementLearning2026}
\citation{liang2023ovseg}
\citation{caesar2018cocostuff}
\citation{chen2015microsoft}
\citation{zhou2017scene}
\citation{mottaghi2014role}
\citation{everingham2015pascal}
\citation{zhou2017scene}
\citation{liang2023ovseg}
\citation{xu2023san}
\citation{caesar2018cocostuff}
\citation{radford2021learning}
\citation{zhou2017scene}
\citation{mottaghi2014role}
\citation{zhou2017scene}
\citation{mottaghi2014role}
\citation{everingham2015pascal}
\citation{xu2023san}
\citation{shiyHarnessingVisionFoundation2025}
\citation{radford2021learning}
\citation{caron2021emerging}
\citation{kirillov2023sam}
\citation{everingham2015pascal}
\citation{mottaghi2014role}
\citation{lin2014microsoft,caesar2018cocostuff}
\citation{cordts2016cityscapes}
\citation{zhou2017scene}
\citation{shiyHarnessingVisionFoundation2025}
\citation{lsunCliperHierarchicallyImproving2025}
\citation{radford2021learning}
\citation{rombach2022high}
\citation{lsunCliperHierarchicallyImproving2025}
\citation{xlaiLisaReasoningSegmentation2024}
\citation{xlaiLisaReasoningSegmentation2024}
\citation{xlaiLisaReasoningSegmentation2024}
\citation{renPixellmPixelReasoning2024}
\citation{renPixellmPixelReasoning2024}
\citation{kirillov2023sam}
\citation{renPixellmPixelReasoning2024}
\citation{zhengVillaVideoReasoning2025}
\citation{zhengVillaVideoReasoning2025}
\citation{zhengVillaVideoReasoning2025}
\citation{zhengVillaVideoReasoning2025}
\citation{libExploringEfficientOpenvocabulary2026}
\citation{libExploringEfficientOpenvocabulary2026}
\citation{libExploringEfficientOpenvocabulary2026}
\citation{yanMedreasonerReinforcementLearning2026}
\citation{yanMedreasonerReinforcementLearning2026}
\citation{yanMedreasonerReinforcementLearning2026}
\citation{wangAffordancer1ReinforcementLearning2026}
\citation{wangAffordancer1ReinforcementLearning2026}
\citation{wangAffordancer1ReinforcementLearning2026}
\citation{wangAffordancer1ReinforcementLearning2026}
\citation{radford2021learning,kirillov2023sam,xlaiLisaReasoningSegmentation2024}
\citation{liang2023ovseg,xu2023san,shiyHarnessingVisionFoundation2025,liu2024groundingdino,renPixellmPixelReasoning2024}
\citation{wuSeeSaySegment2024,guoSeeingBelievingRichContext2026,yanMedreasonerReinforcementLearning2026,wangAffordancer1ReinforcementLearning2026}
\citation{luo2024pnpovss,maaydinItaclipBoostingTrainingfree2025,lsunCliperHierarchicallyImproving2025,dzhangCorrclipReconstructingPatch2025,shiyHarnessingVisionFoundation2025}
\citation{liang2023ovseg}
\citation{liang2023ovseg}
\citation{liang2023ovseg}
\citation{xu2023san,yliMaskadapterDevilMasks2025,xwangUseUniversalSegment2024,xxuanReMEDataCentricFramework2025}
\citation{kirillov2023sam,ravi2024sam2}
\citation{liu2024groundingdino,zhouThinkYouSegment2026,huangUnleashingTemporalspatialReasoning2025}
\@writefile{toc}{\contentsline {section}{\numberline {9}Comparison and Discussion}{30}{section.9}\protected@file@percent }
\newlabel{sec:comparison-discussion}{{9}{30}{Comparison and Discussion}{section.9}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {9.1}Comparison across method families}{30}{subsection.9.1}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {9.2}Semantic openness versus spatial precision}{30}{subsection.9.2}\protected@file@percent }
\@writefile{lof}{\contentsline {figure}{\numberline {19}{\ignorespaces OVSeg's oracle analysis of the two-stage open-vocabulary segmentation bottleneck. This placeholder should be replaced by the original Fig.~1 from \emph {Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP}, which contrasts ordinary CLIP classification on masked regions with an oracle classifier and shows why masked-region recognition, not only mask proposal quality, limits two-stage pipelines~\citep {liang2023ovseg}.}}{30}{figure.19}\protected@file@percent }
\newlabel{fig:comparison-ovseg-oracle-placeholder}{{19}{30}{OVSeg's oracle analysis of the two-stage open-vocabulary segmentation bottleneck. This placeholder should be replaced by the original Fig.~1 from \emph {Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP}, which contrasts ordinary CLIP classification on masked regions with an oracle classifier and shows why masked-region recognition, not only mask proposal quality, limits two-stage pipelines~\citep {liang2023ovseg}}{figure.19}{}}
\citation{liang2023ovseg,xu2023san,yliMaskadapterDevilMasks2025,renPixellmPixelReasoning2024,xlaiLisaReasoningSegmentation2024,zhuLensLearningSegment2026,yanMedreasonerReinforcementLearning2026,wangAffordancer1ReinforcementLearning2026}
\citation{shiyHarnessingVisionFoundation2025}
\citation{lsunCliperHierarchicallyImproving2025}
\citation{xxuanReMEDataCentricFramework2025,luoConnectingDotsTrainingFree2026}
\@writefile{lot}{\contentsline {table}{\numberline {7}{\ignorespaces Comparison of major method families. The table records the interface and the main reporting condition required for fair comparison.}}{31}{table.7}\protected@file@percent }
\newlabel{tab:family-comparison}{{7}{31}{Comparison of major method families. The table records the interface and the main reporting condition required for fair comparison}{table.7}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {9.3}Training-based adaptation versus training-free composition}{31}{subsection.9.3}\protected@file@percent }
\citation{zhuPopenPreferencebasedOptimization2025,zhuLensLearningSegment2026,zhouReasoningImplicitSelfsupervised2026,huangSamr1LeveragingSam2026,xuVideosegr1ReasoningVideo2026,yanMedreasonerReinforcementLearning2026,wangAffordancer1ReinforcementLearning2026}
\citation{xlaiLisaReasoningSegmentation2024}
\citation{renPixellmPixelReasoning2024}
\citation{zhuLensLearningSegment2026,zhouReasoningImplicitSelfsupervised2026}
\@writefile{lot}{\contentsline {table}{\numberline {8}{\ignorespaces Representative quantitative and protocol evidence from original papers. The rows are not directly comparable across tasks; they are included to show the settings behind common comparison claims.}}{32}{table.8}\protected@file@percent }
\newlabel{tab:representative-comparison-evidence}{{8}{32}{Representative quantitative and protocol evidence from original papers. The rows are not directly comparable across tasks; they are included to show the settings behind common comparison claims}{table.8}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {9.4}Reasoning capability versus grounding faithfulness}{32}{subsection.9.4}\protected@file@percent }
\citation{wuSeeSaySegment2024,xiaGsvaGeneralizedSegmentation2024,zhangLlavagroundingGroundedVisual2024,rasheedGlammPixelGrounding2024,huGroundingSuiteMeasuringComplex2025,guoSeeingBelievingRichContext2026}
\citation{zhengVillaVideoReasoning2025,libExploringEfficientOpenvocabulary2026,yanMedreasonerReinforcementLearning2026,wangAffordancer1ReinforcementLearning2026}
\citation{hhuangRenovatingNamesOpenvocabulary2024,ybenigmimFlossFreeLunch2025}
\citation{liang2023ovseg,kirillov2023sam,ravi2024sam2,liu2024groundingdino,xxuanReMEDataCentricFramework2025}
\@writefile{lot}{\contentsline {table}{\numberline {9}{\ignorespaces Representative quantitative and protocol evidence for extended scenarios. These rows use task-specific metrics and should not be compared as a single leaderboard.}}{33}{table.9}\protected@file@percent }
\newlabel{tab:extended-comparison-evidence}{{9}{33}{Representative quantitative and protocol evidence for extended scenarios. These rows use task-specific metrics and should not be compared as a single leaderboard}{table.9}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {9.5}Benchmark and reproducibility gaps}{33}{subsection.9.5}\protected@file@percent }
\citation{radford2021learning,kirillov2023sam,ravi2024sam2,xlaiLisaReasoningSegmentation2024,renPixellmPixelReasoning2024}
\citation{liang2023ovseg,xu2023san,hhuangRenovatingNamesOpenvocabulary2024,ybenigmimFlossFreeLunch2025,shiyHarnessingVisionFoundation2025}
\citation{chngMaskGroundingReferring2024,rasheedGlammPixelGrounding2024,huGroundingSuiteMeasuringComplex2025,yanMedreasonerReinforcementLearning2026,zhengVillaVideoReasoning2025}
\citation{xlaiLisaReasoningSegmentation2024,renPixellmPixelReasoning2024,zhuLensLearningSegment2026,zhouReasoningImplicitSelfsupervised2026}
\citation{xiaGsvaGeneralizedSegmentation2024,wuSeeSaySegment2024,zhangLlavagroundingGroundedVisual2024,guoSeeingBelievingRichContext2026}
\citation{shiyHarnessingVisionFoundation2025,lsunCliperHierarchicallyImproving2025,liu2024groundingdino,luoConnectingDotsTrainingFree2026}
\citation{xu2023san,yliMaskadapterDevilMasks2025,xwangUseUniversalSegment2024,renPixellmPixelReasoning2024,huangSamr1LeveragingSam2026}
\citation{baiOneTokenSeg2024,zhengVillaVideoReasoning2025,chen3DDRESDetailed3D2026,huangSurprise3dDatasetSpatial2026,libExploringEfficientOpenvocabulary2026,yanMedreasonerReinforcementLearning2026,wangAffordancer1ReinforcementLearning2026}
\@writefile{toc}{\contentsline {section}{\numberline {10}Future Directions}{34}{section.10}\protected@file@percent }
\newlabel{sec:future-directions}{{10}{34}{Future Directions}{section.10}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {10.1}Unified and transparent evaluation protocols}{34}{subsection.10.1}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {10.2}Faithful reasoning and verifiable pixel grounding}{34}{subsection.10.2}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {10.3}Efficient and reproducible hybrid segmentation systems}{34}{subsection.10.3}\protected@file@percent }
\citation{zhengVillaVideoReasoning2025,yanMedreasonerReinforcementLearning2026,wangAffordancer1ReinforcementLearning2026}
\citation{kirillov2023sam,ravi2024sam2,rasheedGlammPixelGrounding2024,wuSeeSaySegment2024}
\citation{radford2021learning,kirillov2023sam,ravi2024sam2,liang2023ovseg,xu2023san,xlaiLisaReasoningSegmentation2024,renPixellmPixelReasoning2024}
\bibstyle{elsarticle-num}
\bibdata{references_clean}
\bibcite{long2015fully}{{1}{}{{}}{{}}}
\bibcite{he2017mask}{{2}{}{{}}{{}}}
\bibcite{kirillov2019panoptic}{{3}{}{{}}{{}}}
\bibcite{radford2021learning}{{4}{}{{}}{{}}}
\bibcite{liang2023ovseg}{{5}{}{{}}{{}}}
\bibcite{xu2023san}{{6}{}{{}}{{}}}
\bibcite{xu2023odise}{{7}{}{{}}{{}}}
\bibcite{xshanOpenvocabularySemanticSegmentation2024}{{8}{}{{}}{{}}}
\@writefile{toc}{\contentsline {subsection}{\numberline {10.4}Domain-robust segmentation beyond ordinary 2D images}{35}{subsection.10.4}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {10.5}Human-in-the-loop and accountable segmentation}{35}{subsection.10.5}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {11}Conclusion}{35}{section.11}\protected@file@percent }
\bibcite{xwangUseUniversalSegment2024}{{9}{}{{}}{{}}}
\bibcite{shinhOpenvocabularySemanticSegmentation2024}{{10}{}{{}}{{}}}
\bibcite{yxchngAligningVisionLanguageModel2026}{{11}{}{{}}{{}}}
\bibcite{kirillov2023sam}{{12}{}{{}}{{}}}
\bibcite{ravi2024sam2}{{13}{}{{}}{{}}}
\bibcite{hwangXSAMSegmentAnything2026}{{14}{}{{}}{{}}}
\bibcite{sgxiaoOpenworldsamExtendingSam22026}{{15}{}{{}}{{}}}
\bibcite{xuPixelalignedLanguageModel2024}{{16}{}{{}}{{}}}
\bibcite{zhangLlavagroundingGroundedVisual2024}{{17}{}{{}}{{}}}
\bibcite{youFerretReferGround2024}{{18}{}{{}}{{}}}
\bibcite{rasheedGlammPixelGrounding2024}{{19}{}{{}}{{}}}
\bibcite{lai2024lisa}{{20}{}{{}}{{}}}
\bibcite{renPixellmPixelReasoning2024}{{21}{}{{}}{{}}}
\bibcite{rasheed2024glamm}{{22}{}{{}}{{}}}
\bibcite{luoConnectingDotsTrainingFree2026}{{23}{}{{}}{{}}}
\bibcite{zhuLensLearningSegment2026}{{24}{}{{}}{{}}}
\bibcite{zhouReasoningImplicitSelfsupervised2026}{{25}{}{{}}{{}}}
\bibcite{guoSeeingBelievingRichContext2026}{{26}{}{{}}{{}}}
\bibcite{xuVideosegr1ReasoningVideo2026}{{27}{}{{}}{{}}}
\bibcite{chen3DDRESDetailed3D2026}{{28}{}{{}}{{}}}
\bibcite{libExploringEfficientOpenvocabulary2026}{{29}{}{{}}{{}}}
\bibcite{yanMedreasonerReinforcementLearning2026}{{30}{}{{}}{{}}}
\bibcite{wangAffordancer1ReinforcementLearning2026}{{31}{}{{}}{{}}}
\bibcite{hhuangRenovatingNamesOpenvocabulary2024}{{32}{}{{}}{{}}}
\bibcite{xhanBoostingSegmentAnything2025}{{33}{}{{}}{{}}}
\bibcite{zhuPopenPreferencebasedOptimization2025}{{34}{}{{}}{{}}}
\bibcite{yangExploringCLIPsDense2025}{{35}{}{{}}{{}}}
\bibcite{lee2025escnet}{{36}{}{{}}{{}}}
\bibcite{xlaiLisaReasoningSegmentation2024}{{37}{}{{}}{{}}}
\bibcite{luo2024pnpovss}{{38}{}{{}}{{}}}
\bibcite{xiaoOnerefUnifiedOnetower2024}{{39}{}{{}}{{}}}
\bibcite{zhangOmgllavaBridgingImagelevel2024}{{40}{}{{}}{{}}}
\bibcite{huangPixelsLogicPerceptionReasoning2026}{{41}{}{{}}{{}}}
\bibcite{sikdarPicazoPixelalignedContrastive2025}{{42}{}{{}}{{}}}
\bibcite{yanTaskPreferenceOptimization2025}{{43}{}{{}}{{}}}
\bibcite{huGroundingSuiteMeasuringComplex2025}{{44}{}{{}}{{}}}
\bibcite{vvatsGuidelineConsistentSegmentationMultiAgent2026}{{45}{}{{}}{{}}}
\bibcite{everingham2015pascal}{{46}{}{{}}{{}}}
\bibcite{lin2014microsoft}{{47}{}{{}}{{}}}
\bibcite{caesar2018cocostuff}{{48}{}{{}}{{}}}
\bibcite{zhou2017scene}{{49}{}{{}}{{}}}
\bibcite{cordts2016cityscapes}{{50}{}{{}}{{}}}
\bibcite{noorimTesttimeAdaptationVisionlanguage2026}{{51}{}{{}}{{}}}
\bibcite{ybenigmimFlossFreeLunch2025}{{52}{}{{}}{{}}}
\bibcite{kazemzadeh2014referitgame}{{53}{}{{}}{{}}}
\bibcite{yu2016modeling}{{54}{}{{}}{{}}}
\bibcite{mao2016generation}{{55}{}{{}}{{}}}
\bibcite{chngMaskGroundingReferring2024}{{56}{}{{}}{{}}}
\bibcite{jwangLlmsegBridgingImage2024}{{57}{}{{}}{{}}}
\bibcite{atharVicasDatasetCombining2025}{{58}{}{{}}{{}}}
\bibcite{munasingheVideoglammLargeMultimodal2025}{{59}{}{{}}{{}}}
\bibcite{baiOneTokenSeg2024}{{60}{}{{}}{{}}}
\bibcite{sunSamaMultiturnReferential2026}{{61}{}{{}}{{}}}
\bibcite{yeRISLADBenchmarkModel2026}{{62}{}{{}}{{}}}
\bibcite{quenumLisatLanguageinstructedSegmentation2026}{{63}{}{{}}{{}}}
\bibcite{huangSurprise3dDatasetSpatial2026}{{64}{}{{}}{{}}}
\bibcite{xxuanReMEDataCentricFramework2025}{{65}{}{{}}{{}}}
\bibcite{shiyHarnessingVisionFoundation2025}{{66}{}{{}}{{}}}
\bibcite{xiaGsvaGeneralizedSegmentation2024}{{67}{}{{}}{{}}}
\bibcite{zhangGroundhogGroundingLarge2024}{{68}{}{{}}{{}}}
\bibcite{yuanOspreyPixelUnderstanding2024}{{69}{}{{}}{{}}}
\bibcite{liangReferdinoReferringVideo2025}{{70}{}{{}}{{}}}
\bibcite{zpengParameterefficientFinetuningHyperspherical2025}{{71}{}{{}}{{}}}
\bibcite{yliMaskadapterDevilMasks2025}{{72}{}{{}}{{}}}
\bibcite{zzhaoDPSegDualpromptCost2025}{{73}{}{{}}{{}}}
\bibcite{joseDinov2MeetsText2025}{{74}{}{{}}{{}}}
\bibcite{gejCLIPAdaptedRegiontoTextLearning2025}{{75}{}{{}}{{}}}
\bibcite{maaydinItaclipBoostingTrainingfree2025}{{76}{}{{}}{{}}}
\bibcite{lsunCliperHierarchicallyImproving2025}{{77}{}{{}}{{}}}
\bibcite{dzhangCorrclipReconstructingPatch2025}{{78}{}{{}}{{}}}
\bibcite{rqorbaniSemanticLibraryAdaptation2025}{{79}{}{{}}{{}}}
\bibcite{yliuSteppingOutSimilar2025}{{80}{}{{}}{{}}}
\bibcite{sduttaAerosegHarnessingSam2025}{{81}{}{{}}{{}}}
\bibcite{huangZoRIDiscriminativeZeroshot2025}{{82}{}{{}}{{}}}
\bibcite{zhaoOpenvocabularyRGBThermalSemantic2024}{{83}{}{{}}{{}}}
\bibcite{mleeEffectiveSAMCombination2025}{{84}{}{{}}{{}}}
\bibcite{ymiaoLanghopsLanguageGrounded2026}{{85}{}{{}}{{}}}
\bibcite{dengCoconutpancapJointPanoptic2026}{{86}{}{{}}{{}}}
\bibcite{dukangClassDistributioninducedAttention2025}{{87}{}{{}}{{}}}
\bibcite{sjinFeaturePurificationMatters2025}{{88}{}{{}}{{}}}
\bibcite{qchenTrainingFreeClassPurification2025}{{89}{}{{}}{{}}}
\bibcite{liuEfficientFoundationModel2025}{{90}{}{{}}{{}}}
\bibcite{huangFocsamDelvingDeeply2024}{{91}{}{{}}{{}}}
\bibcite{huangInter2FormerDynamicHybrid2025}{{92}{}{{}}{{}}}
\bibcite{liu2024groundingdino}{{93}{}{{}}{{}}}
\bibcite{zhouThinkYouSegment2026}{{94}{}{{}}{{}}}
\bibcite{huangUnleashingTemporalspatialReasoning2025}{{95}{}{{}}{{}}}
\bibcite{cuttanoSamwiseInfusingWisdom2025}{{96}{}{{}}{{}}}
\bibcite{cshangPromptdrivenReferringImage2024}{{97}{}{{}}{{}}}
\bibcite{daiCurriculumPointPrompting2024}{{98}{}{{}}{{}}}
\bibcite{caoReferAnySegmentation2025}{{99}{}{{}}{{}}}
\bibcite{pengGroundingMultimodalLarge2024}{{100}{}{{}}{{}}}
\bibcite{grosiShowTellBenchmark2025}{{101}{}{{}}{{}}}
\bibcite{tangVisualPositionPrompt2026}{{102}{}{{}}{{}}}
\bibcite{yangRepurposingSAMEfficient2026}{{103}{}{{}}{{}}}
\bibcite{wangIterprimeZeroshotReferring2025}{{104}{}{{}}{{}}}
\bibcite{wangSam2loveSegmentAnything2025}{{105}{}{{}}{{}}}
\bibcite{liuHybridGloballocalRepresentation2025}{{106}{}{{}}{{}}}
\bibcite{qianReasoningAttendTry2025}{{107}{}{{}}{{}}}
\bibcite{feiVitronUnifiedPixellevel2024}{{108}{}{{}}{{}}}
\bibcite{liuUnipixelUnifiedObject2026}{{109}{}{{}}{{}}}
\bibcite{wangAltoAdaptivelengthTokenizer2026}{{110}{}{{}}{{}}}
\bibcite{zongGroundvTeachingVlms2025}{{111}{}{{}}{{}}}
\bibcite{weiInstructsegUnifyingInstructed2025}{{112}{}{{}}{{}}}
\bibcite{jangMmrLargescaleBenchmark2025}{{113}{}{{}}{{}}}
\bibcite{ningEnhancingSpatialReasoning2025}{{114}{}{{}}{{}}}
\bibcite{huangSamr1LeveragingSam2026}{{115}{}{{}}{{}}}
\bibcite{wuSeeSaySegment2024}{{116}{}{{}}{{}}}
\bibcite{shenFinegrainedPreferenceOptimization2026}{{117}{}{{}}{{}}}
\bibcite{zhengVillaVideoReasoning2025}{{118}{}{{}}{{}}}
\bibcite{yingOmnimodalExpressionsReasoning2025}{{119}{}{{}}{{}}}
\bibcite{zhongOmnir1ReinforcementLearning2026}{{120}{}{{}}{{}}}
\bibcite{jianglOpenvocabulary3dSemantic2024}{{121}{}{{}}{{}}}
\bibcite{yyinSai3dSegmentAny2024}{{122}{}{{}}{{}}}
\bibcite{zwangXmask3dCrossmodalMask2024}{{123}{}{{}}{{}}}
\bibcite{mzhouOV3DCGOpenvocabulary3D2025}{{124}{}{{}}{{}}}
\bibcite{kuckrejaGeochatGroundedLarge2024}{{125}{}{{}}{{}}}
\bibcite{chenMIMOMedicalVision2025}{{126}{}{{}}{{}}}
\bibcite{qianAffordancellmGroundingAffordance2024}{{127}{}{{}}{{}}}
\bibcite{zhangOpenhoiOpenworldHandobject2026}{{128}{}{{}}{{}}}
\bibcite{zhengInstructionguidedVisualMasking2024}{{129}{}{{}}{{}}}
\bibcite{zhangFineRSFinegrainedReasoning2026}{{130}{}{{}}{{}}}
\bibcite{chen2015microsoft}{{131}{}{{}}{{}}}
\bibcite{mottaghi2014role}{{132}{}{{}}{{}}}
\bibcite{caron2021emerging}{{133}{}{{}}{{}}}
\bibcite{rombach2022high}{{134}{}{{}}{{}}}
\providecommand\NAT@force@numbers{}\NAT@force@numbers
\gdef \@abspage@last{40}