Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation | Paper Citation Record

Observation 2340345f-fc48-4e17-be70-d4d0b2cc5532 · outbound

This paper cites Temporal-aware Hierarchical Mask Classification for Video Semantic Segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Temporal-aware Hierarchical Mask Classification for Video Semantic Segmentation

Reference 1

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Observation b1b7282d-c040-40b7-b1fd-a307094e690b · outbound

This paper cites Contrastive test-time adaptation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Contrastive test-time adaptation

Reference 2

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This paper cites Per- pixel classification is not all you need for semantic segmen- tation.Advances in neural information processing systems, 34:17864–17875.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Per- pixel classification is not all you need for semantic segmen- tation.Advances in neural information processing systems, 34:17864–17875

Reference 3

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Observation fa35eab2-9c4d-4a87-a79a-0b163e5a838d · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Masked-attention mask transformer for universal image segmentation

Reference 4

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Observation 87f3cf09-204d-40b5-81ea-f275acff1f2f · outbound

This paper cites Finding meaning in points: Weakly super- vised semantic segmentation for event cameras.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Finding meaning in points: Weakly super- vised semantic segmentation for event cameras

Reference 5

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Observation 58f7de57-c3a3-4b50-9c3d-b4418d975b20 · outbound

This paper cites To adapt or not to adapt? real- time adaptation for semantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation To adapt or not to adapt? real- time adaptation for semantic segmentation

Reference 6

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Observation ac510213-2e48-4d02-851d-0e5b2845c867 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation The cityscapes dataset for semantic urban scene understanding

Reference 7

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Observation 2d145831-c3de-443a-9ef1-5a8f59315b02 · outbound

This paper cites Every frame counts: Joint learning of video segmentation and optical flow.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Every frame counts: Joint learning of video segmentation and optical flow

Reference 8

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Observation 01a5d76e-4abc-4d0b-aa91-7434d92570eb · outbound

This paper cites Flownet: Learning optical flow with convolutional networks.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Flownet: Learning optical flow with convolutional networks

Reference 9

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Observation cfce804e-d3fa-4e29-886e-3c9115466028 · outbound

This paper cites Uncertainty reduction for model adaptation in semantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Uncertainty reduction for model adaptation in semantic segmentation

Reference 10

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Observation 8cde8358-5572-4aad-9c89-4767a79fb0ca · outbound

This paper cites Se- mantic video cnns through representation warping.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Se- mantic video cnns through representation warping

Reference 11

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Observation d2e0d78a-4792-4707-90e3-fafc1a12efb8 · outbound

This paper cites Video segmentation with superpixels.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Video segmentation with superpixels

Reference 12

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Observation 56d64b20-77cd-44f3-9b46-8bff50dedb0a · outbound

This paper cites Superpixel-based video ob- ject segmentation using perceptual organization and location prior.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Superpixel-based video ob- ject segmentation using perceptual organization and location prior

Reference 13

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Observation cfef93fe-d76d-44da-b2e8-220b73200e75 · outbound

This paper cites Vanishing-point-guided video semantic segmentation of driving scenes.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Vanishing-point-guided video semantic segmentation of driving scenes

Reference 14

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Observation abc894bb-24e8-4ec0-931b-032bf6f9895e · outbound

This paper cites Exploiting temporal state space sharing for video se- mantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Exploiting temporal state space sharing for video se- mantic segmentation

Reference 15

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Observation 989b305d-be1f-4258-9c8a-465a9478b054 · outbound

This paper cites Temporally distributed networks for fast video semantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Temporally distributed networks for fast video semantic segmentation

Reference 16

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Observation e2f487f8-fb52-4281-85b4-f30a02b1caa0 · outbound

This paper cites Min- vis: A minimal video instance segmentation framework without video-based training.Advances in Neural Informa- tion Processing Systems, 35:31265–31277.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Min- vis: A minimal video instance segmentation framework without video-based training.Advances in Neural Informa- tion Processing Systems, 35:31265–31277

Reference 17

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Observation 98b03f69-eeb8-4418-8b6c-84e0a7daba69 · outbound

This paper cites Efficient uncertainty estimation for se- mantic segmentation in videos.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Efficient uncertainty estimation for se- mantic segmentation in videos

Reference 18

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Observation a912cde0-b507-4f9f-b38c-275bfd0bc529 · outbound

This paper cites Accel: A corrective fusion network for efficient semantic segmenta- tion on video.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Accel: A corrective fusion network for efficient semantic segmenta- tion on video

Reference 19

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Observation f908b736-f173-4edd-b363-46bffee42a7f · outbound

This paper cites Talos: Enhancing semantic scene completion via test-time adaptation on the line of sight.Advances in Neural Information Processing Systems, 37:74211–74232.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Talos: Enhancing semantic scene completion via test-time adaptation on the line of sight.Advances in Neural Information Processing Systems, 37:74211–74232

Reference 20

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Observation 2612273c-7f62-4f43-b140-6b93a78b88d5 · outbound

This paper cites Video scene parsing with predictive feature learn- ing.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Video scene parsing with predictive feature learn- ing

Reference 21

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Observation 4ffcaa7b-afde-4887-b536-c5e0c510c094 · outbound

This paper cites Improved image boundaries for better video segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Improved image boundaries for better video segmentation

Reference 22

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Observation 31bd749a-32c9-498b-80a9-79b9d725e198 · outbound

This paper cites Dc-tta: Divide-and-conquer framework for test-time adaptation of interactive segmenta- tion.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Dc-tta: Divide-and-conquer framework for test-time adaptation of interactive segmenta- tion

Reference 23

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Observation 1f5a15aa-a93d-4a99-bca6-1293854c25eb · outbound

This paper cites Segment any- thing.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Segment any- thing

Reference 24

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Observation 464d4267-0ea6-45f4-9e21-b2080c4adfad · outbound

This paper cites From sam to cams: Ex- ploring segment anything model for weakly supervised se- mantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation From sam to cams: Ex- ploring segment anything model for weakly supervised se- mantic segmentation

Reference 25

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Observation 6f21180a-973f-4ea6-b468-7fb9086e5d3f · outbound

This paper cites Wish: Weakly super- vised instance segmentation using heterogeneous labels.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Wish: Weakly super- vised instance segmentation using heterogeneous labels

Reference 26

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Observation fa20cd0a-6167-43c9-b4f6-64346a5a2520 · outbound

This paper cites Unlocking the potential of ordinary classifier: Class-specific adversarial erasing frame- work for weakly supervised semantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Unlocking the potential of ordinary classifier: Class-specific adversarial erasing frame- work for weakly supervised semantic segmentation

Reference 27

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Observation baa3ce70-2031-4000-ae16-dcb0cf33dd22 · outbound

This paper cites Weakly supervised semantic segmentation via adversarial learning of classifier and reconstructor.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Weakly supervised semantic segmentation via adversarial learning of classifier and reconstructor

Reference 28

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Observation 3f14df5e-cddf-4c8d-917c-019dd93eeea4 · outbound

This paper cites Phase concentration and shortcut suppression for weakly supervised semantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Phase concentration and shortcut suppression for weakly supervised semantic segmentation

Reference 29

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Observation 0df29407-c52f-4203-b34f-296061a2e029 · outbound

This paper cites Gsvnet: Guided spatially-varying convolution for fast semantic seg- mentation on video.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Gsvnet: Guided spatially-varying convolution for fast semantic seg- mentation on video

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:49:55.760908Z

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Observation 05714047-1acf-4920-9bc2-cab5a50fc79e · outbound

This paper cites Video semantic segmentation via sparse temporal transformer.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Video semantic segmentation via sparse temporal transformer

Reference 31

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Observation f085a657-c4f7-45d3-8728-976861932abb · outbound

This paper cites Low-latency video se- mantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Low-latency video se- mantic segmentation

Reference 32

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Observation d2ed0273-b99d-44d3-b2c8-f6dad5197fa7 · outbound

This paper cites Do we really need to access the source data? source hypothesis transfer for un- supervised domain adaptation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Do we really need to access the source data? source hypothesis transfer for un- supervised domain adaptation

Reference 33

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Observation e77a1071-fd04-4c22-8cc2-31dd3732aae4 · outbound

This paper cites LSVOS Challenge 3rd Place Report: SAM2 and Cutie based VOS.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation LSVOS Challenge 3rd Place Report: SAM2 and Cutie based VOS

Reference 34

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Observation cbf0a29f-929c-417f-9993-bf5c066faed9 · outbound

This paper cites Efficient semantic video segmentation with per-frame inference.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Efficient semantic video segmentation with per-frame inference

Reference 35

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Observation 83a5c999-191e-4246-a236-159730fa266e · outbound

This paper cites Spatio-temporal pixel- level contrastive learning-based source-free domain adapta- tion for video semantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Spatio-temporal pixel- level contrastive learning-based source-free domain adapta- tion for video semantic segmentation

Reference 36

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Observation 12daf757-f1e7-4d9b-97da-dc55d2b87bd2 · outbound

This paper cites Vspw: A large-scale dataset for video scene parsing in the wild.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Vspw: A large-scale dataset for video scene parsing in the wild

Reference 37

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Observation 14cf8f29-1ee1-4927-b528-c9fd2a18e197 · outbound

This paper cites Semantic video segmentation by gated recurrent flow propagation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Semantic video segmentation by gated recurrent flow propagation

Reference 38

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Observation d348d0df-ed40-4e83-b558-7bcd0c4e0e5d · outbound

This paper cites Towards stable test-time adaptation in dynamic wild world.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Towards stable test-time adaptation in dynamic wild world

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:44:55.212107Z

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Observation b4ec74c1-f1aa-4606-8f39-071a75c3f271 · outbound

This paper cites Video Object Segmentation via SAM 2: The 4th Solution for LSVOS Challenge VOS Track.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Video Object Segmentation via SAM 2: The 4th Solution for LSVOS Challenge VOS Track

Reference 40

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Observation 1bdafb32-69a4-4aba-bb64-5251c90e7dcd · outbound

This paper cites Real-time, accurate, and consistent video semantic segmentation via unsupervised adaptation and cross-unit de- ployment on mobile device.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Real-time, accurate, and consistent video semantic segmentation via unsupervised adaptation and cross-unit de- ployment on mobile device

Reference 41

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Observation cb834412-86b5-41ca-a60c-bd7464331e8e · outbound

This paper cites Local memory attention for fast video semantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Local memory attention for fast video semantic segmentation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:44:55.205077Z

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Observation e1315f48-7278-4698-95c4-b0f9e0fd7d41 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation SAM 2: Segment Anything in Images and Videos

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-05-11T09:00:58.781073Z

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Observation 0eeb066d-9691-47d0-9982-3be3dccdc030 · outbound

This paper cites Motion-state alignment for video semantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Motion-state alignment for video semantic segmentation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:44:55.198662Z

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Observation f0905360-6ca0-4cdd-bf78-580548983e12 · outbound

This paper cites Coarse-to-fine feature mining for video se- mantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Coarse-to-fine feature mining for video se- mantic segmentation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:44:55.185806Z

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Observation 5741aace-732b-4fe6-bb16-cf08b0e27485 · outbound

This paper cites Mining relations among cross-frame affinities for video semantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Mining relations among cross-frame affinities for video semantic segmentation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:44:55.189009Z

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Observation ae8b9fb8-b64c-4040-8709-9976d2c28d52 · outbound

This paper cites Learning local and global temporal contexts for video semantic segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Learning local and global temporal contexts for video semantic segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:44:55.182685Z

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Observation 6e1e8ff9-e025-4c4a-8d8a-91360da1c03d · outbound

This paper cites Tesla: Test-time self-learning with automatic adversarial augmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Tesla: Test-time self-learning with automatic adversarial augmentation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:44:55.179366Z

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Observation 75c0aae2-eb43-48e9-9e4b-f615378ca406 · outbound

This paper cites The 2nd Solution for LSVOS Challenge RVOS Track: Spatial-temporal Refinement for Consistent Semantic Segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation The 2nd Solution for LSVOS Challenge RVOS Track: Spatial-temporal Refinement for Consistent Semantic Segmentation

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:00:58.775625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6cd2aa0c-0159-4f0e-abf5-d88db5670cac · outbound

This paper cites Unsupervised semantic seg- mentation by contrasting object mask proposals.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Unsupervised semantic seg- mentation by contrasting object mask proposals

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:44:55.222460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9ac29b5e-d194-4b4d-87f4-9735280cb377 · outbound

This paper cites Multiple hypothesis video segmentation from superpixel flows.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Multiple hypothesis video segmentation from superpixel flows

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:44:55.192273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cbc4d598-0084-4757-a2e7-f5303e1a3b78 · outbound

This paper cites On the road to online adaptation for semantic image segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation On the road to online adaptation for semantic image segmentation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:44:55.201917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 93f18233-fd76-4003-9d0e-be13755b198a · outbound

This paper cites Tent: Fully Test-time Adaptation by Entropy Minimization.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Tent: Fully Test-time Adaptation by Entropy Minimization

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-16T10:09:24.402331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6386d510-8ee9-41db-bb73-4398deee9b63 · outbound

This paper cites Temporal memory attention for video semantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Temporal memory attention for video semantic segmentation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:44:55.176235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5f299a24-f405-4371-8031-fd48b4f0170d · outbound

This paper cites Continual test-time domain adaptation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Continual test-time domain adaptation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:44:55.169846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2767484c-ed6f-4c98-8d37-a51d9ff90ff3 · outbound

This paper cites Dynamically instance- guided adaptation: A backward-free approach for test-time domain adaptive semantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Dynamically instance- guided adaptation: A backward-free approach for test-time domain adaptive semantic segmentation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:44:55.160332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0c92cdff-97a5-4967-af82-b3e2e9d334c3 · outbound

This paper cites Continual test-time domain adaptation via dynamic sample selection.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Continual test-time domain adaptation via dynamic sample selection

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:44:55.166788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation be298149-f507-4d17-bdfb-f96a26edefa2 · outbound

This paper cites Mask propagation for efficient video semantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Mask propagation for efficient video semantic segmentation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:44:55.163635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 09cc2565-5437-476b-b5a7-32f2d6d386c7 · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transform- ers.Advances in neural information processing systems, 34: 12077–12090.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Segformer: Simple and efficient design for semantic segmentation with transform- ers.Advances in neural information processing systems, 34: 12077–12090

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:49:55.730508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b572f900-2a22-4cb7-8f1f-316aa9ac7ecb · outbound

This paper cites an unresolved cited work.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-05-17T15:49:55.753683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7c0f0201-952c-4a47-9d65-d7ec2b8c3ddb · outbound

This paper cites Entitysam: Segment everything in video.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Entitysam: Segment everything in video

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:49:55.617641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 607b5e8d-99fb-494c-8563-67a1cfa210d2 · outbound

This paper cites Adversarial erasing frame- work via triplet with gated pyramid pooling layer for weakly supervised semantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Adversarial erasing frame- work via triplet with gated pyramid pooling layer for weakly supervised semantic segmentation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:49:55.743768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 994d288f-224c-4178-877a-92fbbeabc5dc · outbound

This paper cites Diffusion-guided weakly super- vised semantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Diffusion-guided weakly super- vised semantic segmentation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:49:55.629819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d3d2cc35-e499-4d91-8130-98ba255c196d · outbound

This paper cites Class tokens infusion for weakly supervised semantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Class tokens infusion for weakly supervised semantic segmentation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:49:55.750936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c561b15b-f9a1-409f-b8d8-5dfa9adaae05 · outbound

This paper cites SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation

Reference 65

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T09:00:58.817814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4c63cea5-cd61-469a-b017-d4e4bcba6900 · outbound

This paper cites Object- contextual representations for semantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Object- contextual representations for semantic segmentation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:44:55.156772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9c529518-e553-460c-8a8f-e7852100e0ea · outbound

This paper cites Underwater Camouflaged Object Tracking Meets Vision-Language SAM2.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Underwater Camouflaged Object Tracking Meets Vision-Language SAM2

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:00:58.768562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 62538aad-0a3a-4f87-bc20-65f32cb2c62e · outbound

This paper cites Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:00:58.826021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d87f0420-74f2-4b20-a8dd-29b5bc2873a0 · outbound

This paper cites Auxadapt: Stable and efficient test-time adaptation for temporally consistent video semantic segmentation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Auxadapt: Stable and efficient test-time adaptation for temporally consistent video semantic segmentation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:44:55.173000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 50a20b6a-5fdb-4ab6-8582-ba284a02e445 · outbound

This paper cites Scene parsing through ade20k dataset.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Scene parsing through ade20k dataset

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T15:44:55.208786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e4951577-bde0-4548-9b4f-2aa9f5376c4a · outbound

This paper cites When SAM2 Meets Video Camouflaged Object Segmentation: A Comprehensive Evaluation and Adaptation.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation When SAM2 Meets Video Camouflaged Object Segmentation: A Comprehensive Evaluation and Adaptation

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:00:58.796445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Reference resolution

Outbound references

Pith citing papers