Autoregressive Universal Video Segmentation Model | Paper Citation Record

Observation 86b416ae-2ac7-42d1-906c-40a7941ad941 · outbound

This paper cites Just read twice: closing the recall gap for recurrent language models.

Autoregressive Universal Video Segmentation Model Just read twice: closing the recall gap for recurrent language models

Reference 1

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Observation ae6d0bf0-1b02-4538-841f-12baaedb2af1 · outbound

This paper cites Tarvis: A unified approach for target-based video segmentation.

Autoregressive Universal Video Segmentation Model Tarvis: A unified approach for target-based video segmentation

Reference 2

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Observation ca615088-376e-49bc-9125-482cc92f49a8 · outbound

This paper cites End-to-end object detection with transformers.

Autoregressive Universal Video Segmentation Model End-to-end object detection with transformers

Reference 3

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Observation 97f0ece7-c232-4467-a313-477f1bf25b58 · outbound

This paper cites Per-pixel classification is not all you need for semantic segmentation.

Autoregressive Universal Video Segmentation Model Per-pixel classification is not all you need for semantic segmentation

Reference 4

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Observation b9172dd7-93d9-4e20-bdb8-7d22ae9c4c11 · outbound

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

Autoregressive Universal Video Segmentation Model Masked-attention mask transformer for universal image segmentation

Reference 5

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Observation 7913f846-7918-44f7-a6cc-7bcf07e5d27c · outbound

This paper cites Xmem: Long-term video object segmentation with an atkinson- shiffrin memory model.

Autoregressive Universal Video Segmentation Model Xmem: Long-term video object segmentation with an atkinson- shiffrin memory model

Reference 6

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Observation f6c0c31c-4410-4c91-932d-c6b70941c826 · outbound

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

Autoregressive Universal Video Segmentation Model The cityscapes dataset for semantic urban scene understanding

Reference 7

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Observation 4731e06c-f2f2-47e9-bae1-045836d56a9c · outbound

This paper cites Mose: A new dataset for video object segmentation in complex scenes.

Autoregressive Universal Video Segmentation Model Mose: A new dataset for video object segmentation in complex scenes

Reference 8

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Observation bb0d6999-317a-475b-9b9a-693a1fadbb33 · outbound

This paper cites The Llama 3 Herd of Models.

Autoregressive Universal Video Segmentation Model The Llama 3 Herd of Models

Reference 9

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Observation 2649edb2-2207-4798-9572-e6178d0e3a6c · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Autoregressive Universal Video Segmentation Model Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 10

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Observation d91cf4ff-0e1e-4bb8-bc3b-92b85d64b33e · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Autoregressive Universal Video Segmentation Model Efficiently Modeling Long Sequences with Structured State Spaces

Reference 11

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Observation 57c9f109-2d46-4f4e-bbcf-e234a9c7b385 · outbound

This paper cites On the parameterization and initialization of diagonal state space models.

Autoregressive Universal Video Segmentation Model On the parameterization and initialization of diagonal state space models

Reference 12

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Observation 2d6e736f-e66b-42c0-9932-5169ac518a0f · outbound

This paper cites Vita: Video instance segmentation via object token association.

Autoregressive Universal Video Segmentation Model Vita: Video instance segmentation via object token association

Reference 13

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Observation 4a4bd33f-9d05-4b97-861b-8397af59a42d · outbound

This paper cites A generalized framework for video instance segmentation.

Autoregressive Universal Video Segmentation Model A generalized framework for video instance segmentation

Reference 14

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Observation d08d82a7-5b73-4a83-ba97-14940e838a40 · outbound

This paper cites Omni-rgpt: Unifyingimageandvideoregion-levelunderstanding via token marks.

Autoregressive Universal Video Segmentation Model Omni-rgpt: Unifyingimageandvideoregion-levelunderstanding via token marks

Reference 15

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Observation e5c62569-62d9-46f9-9be6-c0416591949a · outbound

This paper cites Robust and consistent online video instance segmentation via instance mask propagation.

Autoregressive Universal Video Segmentation Model Robust and consistent online video instance segmentation via instance mask propagation

Reference 16

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Observation cd06ca60-2ec1-4a76-b8b5-03085f6dc174 · outbound

This paper cites RULER: What's the Real Context Size of Your Long-Context Language Models?.

Autoregressive Universal Video Segmentation Model RULER: What's the Real Context Size of Your Long-Context Language Models?

Reference 17

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Observation c0b308c0-6515-4f70-9e8f-71e5b060850d · outbound

This paper cites Minvis: A minimal video instance segmentation framework without video-based training.

Autoregressive Universal Video Segmentation Model Minvis: A minimal video instance segmentation framework without video-based training

Reference 18

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Observation 7b45a9d9-c36c-44d2-ad63-e64170754081 · outbound

This paper cites Video instance segmentation using inter-frame communication transformers.

Autoregressive Universal Video Segmentation Model Video instance segmentation using inter-frame communication transformers

Reference 19

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Observation 4825247a-88c9-482d-93e6-d78cb83ab75b · outbound

This paper cites Video panoptic segmentation.

Autoregressive Universal Video Segmentation Model Video panoptic segmentation

Reference 20

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Observation b8594234-570c-40c2-aac4-daf8e1bb48b6 · outbound

This paper cites Tubeformer-deeplab: Video mask transformer.

Autoregressive Universal Video Segmentation Model Tubeformer-deeplab: Video mask transformer

Reference 21

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Observation e0771a88-4d8d-4fe6-ac32-a5f5d94b833d · outbound

This paper cites Visage: Video instance segmentation with appearance-guided enhancement.

Autoregressive Universal Video Segmentation Model Visage: Video instance segmentation with appearance-guided enhancement

Reference 22

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Observation 1461a5e3-f4d3-4260-8759-8a302446bdf3 · outbound

This paper cites Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick.

Autoregressive Universal Video Segmentation Model Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick

Reference 23

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Observation afc8ab21-2689-439d-b37f-0d5b105190ea · outbound

This paper cites Univs: Unified and universal video segmentation with prompts as queries.

Autoregressive Universal Video Segmentation Model Univs: Unified and universal video segmentation with prompts as queries

Reference 24

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Observation fbd9a8f8-6014-45d1-b787-f737545eb36d · outbound

This paper cites Video k-net: A simple, strong, and unified baseline for video segmentation.

Autoregressive Universal Video Segmentation Model Video k-net: A simple, strong, and unified baseline for video segmentation

Reference 25

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Observation 5bd90744-5653-4830-812d-1e2451df1eb7 · outbound

This paper cites Microsoft coco: Common objects in context.

Autoregressive Universal Video Segmentation Model Microsoft coco: Common objects in context

Reference 26

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Observation 51e38b0e-7f0e-4874-bb39-8c9463bf3a93 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Autoregressive Universal Video Segmentation Model Swin transformer: Hierarchical vision transformer using shifted windows

Reference 27

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Observation 891f8809-64ee-4748-857a-a44bfab5c832 · outbound

This paper cites Decoupled weight decay regularization.

Autoregressive Universal Video Segmentation Model Decoupled weight decay regularization

Reference 28

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Observation e0e281c9-c11e-4333-bc78-8b1c9d2113c8 · outbound

This paper cites Language Models are Few-Shot Learners.

Autoregressive Universal Video Segmentation Model Language Models are Few-Shot Learners

Reference 29

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Observation d723adf5-6981-48f6-a702-07d497284ddd · outbound

This paper cites Trackformer: Multi- object tracking with transformers.

Autoregressive Universal Video Segmentation Model Trackformer: Multi- object tracking with transformers

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:17.326155Z

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Observation 39084838-b895-49db-b5e9-23042f0ea2ea · outbound

This paper cites MOT16: A Benchmark for Multi-Object Tracking.

Autoregressive Universal Video Segmentation Model MOT16: A Benchmark for Multi-Object Tracking

Reference 31

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Observation 96b0c5b9-0c8b-46d3-a322-5775d6c38d19 · outbound

This paper cites Videoobjectsegmentationusingspace-time memory networks.

Autoregressive Universal Video Segmentation Model Videoobjectsegmentationusingspace-time memory networks

Reference 32

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Observation ffe03473-a0b6-406e-9123-b4b74133be84 · outbound

This paper cites The 2017 DAVIS Challenge on Video Object Segmentation.

Autoregressive Universal Video Segmentation Model The 2017 DAVIS Challenge on Video Object Segmentation

Reference 33

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Observation 17033fd4-ea4f-40b0-a618-7550bc36d650 · outbound

This paper cites Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation.

Autoregressive Universal Video Segmentation Model Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation

Reference 34

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Observation 97f69a1b-300c-4796-bfc5-f0a32e6de72c · outbound

This paper cites Occluded video instance segmentation: A benchmark.IJCV, 2022.

Autoregressive Universal Video Segmentation Model Occluded video instance segmentation: A benchmark.IJCV, 2022

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:17.292854Z

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Observation a27ccbf2-f2a7-4e03-9083-c16f18856fc8 · outbound

This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019.

Autoregressive Universal Video Segmentation Model Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:17.277081Z

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Observation ddc4f406-bde8-4145-93bb-a8f4f4d39be0 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Autoregressive Universal Video Segmentation Model Learning transferable visual models from natural language supervision

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:17.261577Z

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Observation 9cfa00d0-639a-4296-a769-b3d9dbd209c6 · outbound

This paper cites Sam 2: Segment anything in images and videos.

Autoregressive Universal Video Segmentation Model Sam 2: Segment anything in images and videos

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:17.244625Z

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Observation 5689917a-dba1-45b7-8618-18c96592be45 · outbound

This paper cites Urvos: Unified referring video object segmentation network with a large-scale benchmark.

Autoregressive Universal Video Segmentation Model Urvos: Unified referring video object segmentation network with a large-scale benchmark

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:17.227593Z

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Observation 8b14a890-e01d-43b7-a6a8-ee613d1820c0 · outbound

This paper cites Repetition Improves Language Model Embeddings.

Autoregressive Universal Video Segmentation Model Repetition Improves Language Model Embeddings

Reference 40

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Observation 57c5c273-9677-46b0-bf01-f88a32608fa7 · outbound

This paper cites Sequence to sequence learning with neural networks.

Autoregressive Universal Video Segmentation Model Sequence to sequence learning with neural networks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:17.209269Z

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Observation 58d6caf9-96d9-4f37-8235-aa98213c07f8 · outbound

This paper cites Attention is all you need.

Autoregressive Universal Video Segmentation Model Attention is all you need

Reference 42

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Observation 471ab83e-d319-43c1-b713-0b7ad505bc3d · outbound

This paper cites Mots: Multi-object tracking and segmentation.

Autoregressive Universal Video Segmentation Model Mots: Multi-object tracking and segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:17.174807Z

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Observation fe02fd67-d773-4f2d-8c45-b940e4c0ba9f · outbound

This paper cites Max-deeplab: End-to-end panoptic segmentation with mask transformers.

Autoregressive Universal Video Segmentation Model Max-deeplab: End-to-end panoptic segmentation with mask transformers

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:17.158195Z

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Observation 1b47c41b-c718-4f3a-9541-ffefbc1331ad · outbound

This paper cites End-to-end video instance segmentation with transformers.

Autoregressive Universal Video Segmentation Model End-to-end video instance segmentation with transformers

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:17.141749Z

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Observation 88c1a9fb-1e2e-42e3-93d4-4ee22bdb26ff · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Autoregressive Universal Video Segmentation Model Chain-of-thought prompting elicits reasoning in large language models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:17.122859Z

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Observation 54cc4cc7-8dac-482e-8275-4e8218799bdf · outbound

This paper cites Segment every reference object in spatial and temporal spaces.

Autoregressive Universal Video Segmentation Model Segment every reference object in spatial and temporal spaces

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:17.105300Z

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Observation 12536e1d-f8d0-46ec-b7a0-6669087dc48b · outbound

This paper cites UniRef++: Segment Every Reference Object in Spatial and Temporal Spaces.

Autoregressive Universal Video Segmentation Model UniRef++: Segment Every Reference Object in Spatial and Temporal Spaces

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-05T15:52:16.637223Z

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Observation b8c805a8-9f0d-4803-b494-4103fd5a4c78 · outbound

This paper cites In defense of online models for video instance segmentation.

Autoregressive Universal Video Segmentation Model In defense of online models for video instance segmentation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:17.086960Z

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Observation 03fabcbb-02d6-453d-814e-9316fd8407af · outbound

This paper cites Online object tracking: A benchmark.

Autoregressive Universal Video Segmentation Model Online object tracking: A benchmark

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:17.067808Z

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Observation 99af8edc-eff1-4366-8650-221d764ec550 · outbound

This paper cites Efficient Streaming Language Models with Attention Sinks.

Autoregressive Universal Video Segmentation Model Efficient Streaming Language Models with Attention Sinks

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-05T15:52:16.653696Z

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Observation 69fb682e-a55a-4b1f-acab-f281f0b2c7b8 · outbound

This paper cites YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark.

Autoregressive Universal Video Segmentation Model YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T15:52:16.659221Z

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Observation 5eb3c589-7670-4912-8c20-f706e56b01f4 · outbound

This paper cites Universal instance perception as object discovery and retrieval.

Autoregressive Universal Video Segmentation Model Universal instance perception as object discovery and retrieval

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:17.050220Z

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Observation 41f27a09-3114-4a91-aca5-58a7a9d5f584 · outbound

This paper cites Video instance segmentation.

Autoregressive Universal Video Segmentation Model Video instance segmentation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:17.030729Z

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Observation 7ca2f19f-1bc9-49ea-b5be-af608e24fff5 · outbound

This paper cites Decoupling features in hierarchical propagation for video object segmentation.

Autoregressive Universal Video Segmentation Model Decoupling features in hierarchical propagation for video object segmentation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:17.011198Z

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Observation 2e1593db-fa4d-45ad-a308-0788bd75f222 · outbound

This paper cites Associating objects with transformers for video object segmen- tation.

Autoregressive Universal Video Segmentation Model Associating objects with transformers for video object segmen- tation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:16.992951Z

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Observation d813b1bd-be76-44b6-85a5-45ccd77f5dd2 · outbound

This paper cites Ctvis: Consistent training for online video instance segmentation.

Autoregressive Universal Video Segmentation Model Ctvis: Consistent training for online video instance segmentation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:16.975710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation aaf548a7-c725-4cef-932b-04afca00c196 · outbound

This paper cites Dvis: Decoupled video instance segmentation framework.

Autoregressive Universal Video Segmentation Model Dvis: Decoupled video instance segmentation framework

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:52:16.957792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers