X2SAM: Any Segmentation in Images and Videos | Paper Citation Record

Observation 189a533b-936a-4955-8d15-8da0e87348e6 · outbound

This paper cites Qwen Technical Report.

X2SAM: Any Segmentation in Images and Videos Qwen Technical Report

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-11T15:01:05.310910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ee46ec15-f3da-40f0-beb6-71859a1d303e · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

X2SAM: Any Segmentation in Images and Videos LLaMA: Open and Efficient Foundation Language Models

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-11T15:01:05.240526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 112e7e39-cdcb-4a74-bed1-3d0328ae86cd · outbound

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

X2SAM: Any Segmentation in Images and Videos Learning transferable visual models from natural language supervision

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.536243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d661328e-2fc3-4b90-9f66-18185220cd93 · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision.

X2SAM: Any Segmentation in Images and Videos Scaling up visual and vision-language representation learning with noisy text supervision

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.498229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1a9c4b47-0ff5-4ef6-9da4-ce5691db9d97 · outbound

This paper cites Show, attend and tell: Neural image caption generation with visual attention.

X2SAM: Any Segmentation in Images and Videos Show, attend and tell: Neural image caption generation with visual attention

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.539604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation efbb2c68-ac56-46c8-9834-5c011cd288bd · outbound

This paper cites Vqa: Visual question answering.

X2SAM: Any Segmentation in Images and Videos Vqa: Visual question answering

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.494715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8a666af3-d6f6-40ee-95c4-935b067aeaff · outbound

This paper cites Language-based image editing with recurrent attentive models.

X2SAM: Any Segmentation in Images and Videos Language-based image editing with recurrent attentive models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.501296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 73d5b524-410c-4d1c-a816-5c329c2a46cb · outbound

This paper cites Segment anything.

X2SAM: Any Segmentation in Images and Videos Segment anything

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.504364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 57511c89-3be4-4704-b86d-a1b3c87186d1 · outbound

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

X2SAM: Any Segmentation in Images and Videos SAM 2: Segment Anything in Images and Videos

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-11T15:01:05.356359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9a1001aa-4c7b-42f2-8239-3d622cc1ab50 · outbound

This paper cites Lisa: Reasoning segmentation via large language model.

X2SAM: Any Segmentation in Images and Videos Lisa: Reasoning segmentation via large language model

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.510978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 83a0d30e-093f-4cc7-8039-969fd5a5b955 · outbound

This paper cites Visa: Reasoning video object segmentation via large language models.ECCV.

X2SAM: Any Segmentation in Images and Videos Visa: Reasoning video object segmentation via large language models.ECCV

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.476693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d02ec514-18dc-494d-abc9-69eba5c3511d · outbound

This paper cites One token to seg them all: Language instructed reasoning segmentation in videos.NeurIPS.

X2SAM: Any Segmentation in Images and Videos One token to seg them all: Language instructed reasoning segmentation in videos.NeurIPS

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.480003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ef49ba74-329f-4077-92a8-5222ed275bfb · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

X2SAM: Any Segmentation in Images and Videos Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.483298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 843c713f-225c-47ee-b7f7-0f8ab3275bbc · outbound

This paper cites Improved baselines with visual instruction tuning.

X2SAM: Any Segmentation in Images and Videos Improved baselines with visual instruction tuning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.514451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c95311b6-83b1-40e2-8254-c9c2a49a22b4 · outbound

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

X2SAM: Any Segmentation in Images and Videos Tarvis: A unified approach for target-based video segmentation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.521963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation aeba86b3-ffcb-4e68-b3b7-56a9ca1126c1 · outbound

This paper cites Oneformer: One transformer to rule universal image segmentation.

X2SAM: Any Segmentation in Images and Videos Oneformer: One transformer to rule universal image segmentation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.528451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2df7d21c-1b2b-4b3b-9f7c-1ade3bd75d29 · outbound

This paper cites Omg-seg: Is one model good enough for all segmentation? InCVPR, pages 27948–27959.

X2SAM: Any Segmentation in Images and Videos Omg-seg: Is one model good enough for all segmentation? InCVPR, pages 27948–27959

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.682610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c7c79173-c57b-4342-bb2f-8493a602bb72 · outbound

This paper cites Omg-llava: Bridging image-level, object-level, pixel-level reasoning and understanding.NeurIPS, 37:71737–71767.

X2SAM: Any Segmentation in Images and Videos Omg-llava: Bridging image-level, object-level, pixel-level reasoning and understanding.NeurIPS, 37:71737–71767

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.678888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 00024a58-bc61-40ad-8391-b81693f6e39e · outbound

This paper cites Temporal memory attention for video semantic segmentation.

X2SAM: Any Segmentation in Images and Videos Temporal memory attention for video semantic segmentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.686154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f512585c-0589-43c6-ae74-50191e885586 · outbound

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

X2SAM: Any Segmentation in Images and Videos Video k-net: A simple, strong, and unified baseline for video segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.693255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation af656a47-b134-4013-b0d3-5e5c5ed71abe · outbound

This paper cites X-sam: From segment anything to any segmentation.

X2SAM: Any Segmentation in Images and Videos X-sam: From segment anything to any segmentation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.667366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6974a5d1-5310-4dd2-baeb-57ed19f590ea · outbound

This paper cites Visual instruction tuning.NeurIPS, 36:34892–34916.

X2SAM: Any Segmentation in Images and Videos Visual instruction tuning.NeurIPS, 36:34892–34916

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.671074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 64660bb0-4f1c-4366-924a-f0b000bbab47 · outbound

This paper cites Llavanext: Improved reasoning, ocr, and world knowledge.

X2SAM: Any Segmentation in Images and Videos Llavanext: Improved reasoning, ocr, and world knowledge

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.518515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 02ce4c79-32d9-49c6-941c-5a1e0530e1d5 · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

X2SAM: Any Segmentation in Images and Videos LLaVA-OneVision: Easy Visual Task Transfer

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-11T15:01:05.327718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c1ff8307-044d-4a7e-be60-6a2c5bfcf9cf · outbound

This paper cites How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites.Science China Information Sciences, 67(12):220101.

X2SAM: Any Segmentation in Images and Videos How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites.Science China Information Sciences, 67(12):220101

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.660214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 896b17eb-d5e7-4d8f-939c-172841dd903a · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

X2SAM: Any Segmentation in Images and Videos Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-11T15:01:05.456688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8a919a35-e3c3-4889-8b0a-33b4989eb4b0 · outbound

This paper cites Glamm: Pixel grounding large multimodal model.

X2SAM: Any Segmentation in Images and Videos Glamm: Pixel grounding large multimodal model

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.652775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0196f2a1-bdb6-4125-8449-43c5cd76b280 · outbound

This paper cites Videoglamm: A large multimodal model for pixel-level visual grounding in videos.CVPR.

X2SAM: Any Segmentation in Images and Videos Videoglamm: A large multimodal model for pixel-level visual grounding in videos.CVPR

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.656287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation af0980ee-7629-4667-9e72-759526988977 · outbound

This paper cites Psalm: Pixelwise segmentation with large multi-modal model.

X2SAM: Any Segmentation in Images and Videos Psalm: Pixelwise segmentation with large multi-modal model

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.663736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bba4b529-0457-41d8-84ff-7f7bc8d4facf · outbound

This paper cites HyperSeg: Towards Universal Visual Segmentation with Large Language Model.

X2SAM: Any Segmentation in Images and Videos HyperSeg: Towards Universal Visual Segmentation with Large Language Model

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:01:05.436176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b81759f9-1147-4d4a-819b-121ec2ec0a0b · outbound

This paper cites Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos.

X2SAM: Any Segmentation in Images and Videos Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-16T11:39:22.805579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f2b0c637-ef50-418c-992d-c7bd6da00cb1 · outbound

This paper cites Qwen3-VL Technical Report.

X2SAM: Any Segmentation in Images and Videos Qwen3-VL Technical Report

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-05-11T15:01:05.383742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ec346914-1640-4eef-afb5-371ccabdbf0e · outbound

This paper cites Ferret: Refer and Ground Anything Anywhere at Any Granularity.

X2SAM: Any Segmentation in Images and Videos Ferret: Refer and Ground Anything Anywhere at Any Granularity

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-15T11:29:05.422888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 426e0b0d-f9b4-4e99-9fbf-3abad408de99 · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.

X2SAM: Any Segmentation in Images and Videos V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.674761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0e9620a1-d871-43b7-bbab-e92b044516c7 · outbound

This paper cites Improving language understanding by generative pre-training.

X2SAM: Any Segmentation in Images and Videos Improving language understanding by generative pre-training

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.635492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 48ae41de-27a5-4684-b2ce-c0d10650fb2b · outbound

This paper cites Focal loss for dense object detection.

X2SAM: Any Segmentation in Images and Videos Focal loss for dense object detection

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.639720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5714d776-dbe0-48c9-a429-e69de5135a60 · outbound

This paper cites Large-scale video panoptic segmentation in the wild: A benchmark.

X2SAM: Any Segmentation in Images and Videos Large-scale video panoptic segmentation in the wild: A benchmark

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.627025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ecd7e4e6-cd0b-47d2-be74-ce892fff8d39 · outbound

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

X2SAM: Any Segmentation in Images and Videos Vspw: A large-scale dataset for video scene parsing in the wild

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.619200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b59f63e8-6e66-4f52-ba88-c9b6cacba9d5 · outbound

This paper cites Video instance segmentation.

X2SAM: Any Segmentation in Images and Videos Video instance segmentation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.622865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9cf3bdca-9cc3-4380-b93c-f4fa1c233029 · outbound

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

X2SAM: Any Segmentation in Images and Videos Urvos: Unified referring video object segmentation network with a large-scale benchmark

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.631094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2cb0fd27-26fd-4a0c-a948-00e046bcd75b · outbound

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

X2SAM: Any Segmentation in Images and Videos YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:01:05.428048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1b3a79e1-793e-47b5-bfbb-8acd67dd255f · outbound

This paper cites A benchmark dataset and evaluation methodology for video object segmentation.

X2SAM: Any Segmentation in Images and Videos A benchmark dataset and evaluation methodology for video object segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.642997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 979af393-7e8b-4cc5-8da4-0f631655a1df · outbound

This paper cites Video-chatgpt: Towards detailed video understanding via large vision and language models.

X2SAM: Any Segmentation in Images and Videos Video-chatgpt: Towards detailed video understanding via large vision and language models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.646842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4ff49862-0d0c-4d00-bc18-6d5cc73c35f3 · outbound

This paper cites Gres: Generalized referring expression segmentation.

X2SAM: Any Segmentation in Images and Videos Gres: Generalized referring expression segmentation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.689595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c65917f6-a72f-48a0-9a66-37df14f5e1ea · outbound

This paper cites Semantic understanding of scenes through the ade20k dataset.IJCV, 127(3):302–321.

X2SAM: Any Segmentation in Images and Videos Semantic understanding of scenes through the ade20k dataset.IJCV, 127(3):302–321

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.696795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a8702369-aeaf-4897-814e-cf6494515560 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

X2SAM: Any Segmentation in Images and Videos LoRA: Low-rank adaptation of large language models

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.598446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d1bf84a3-8b50-4e07-bb31-8e73d1a805c3 · outbound

This paper cites Decoupled Weight Decay Regularization.

X2SAM: Any Segmentation in Images and Videos Decoupled Weight Decay Regularization

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-05-11T15:01:05.228126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 185942e1-e13f-4040-8fc0-7373101d478f · outbound

This paper cites Open-vocabulary panoptic segmentation with text-to-image diffusion models.

X2SAM: Any Segmentation in Images and Videos Open-vocabulary panoptic segmentation with text-to-image diffusion models

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.580826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5775c423-951a-4a6d-866a-f7911a22f73e · outbound

This paper cites Uniref++: Segment every reference object in spatial and temporal spaces.ICCV.

X2SAM: Any Segmentation in Images and Videos Uniref++: Segment every reference object in spatial and temporal spaces.ICCV

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.486963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ddd436b2-739a-471c-bb30-79590767642d · outbound

This paper cites Unipixel: Unified object referring and segmentation for pixel-level visual reasoning.

X2SAM: Any Segmentation in Images and Videos Unipixel: Unified object referring and segmentation for pixel-level visual reasoning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.491118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5442f6fb-7a08-4af4-be2b-a5640366d55e · outbound

This paper cites Segment everything everywhere all at once.NeurIPS, 36:19769–19782.

X2SAM: Any Segmentation in Images and Videos Segment everything everywhere all at once.NeurIPS, 36:19769–19782

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.507529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9bc49f70-74a6-4281-ae04-8ba5b8871e34 · outbound

This paper cites Language as queries for referring video object segmentation.

X2SAM: Any Segmentation in Images and Videos Language as queries for referring video object segmentation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.525512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2af7add8-b3e8-4b47-9326-6b86450a8b53 · outbound

This paper cites LaSagnA: Language-based Segmentation Assistant for Complex Queries.

X2SAM: Any Segmentation in Images and Videos LaSagnA: Language-based Segmentation Assistant for Complex Queries

Reference 55

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:01:05.418347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fc734836-8251-4641-914b-d10e09e118b0 · outbound

This paper cites Mmbench: Is your multi-modal model an all-around player? InECCV, pages 216–233.

X2SAM: Any Segmentation in Images and Videos Mmbench: Is your multi-modal model an all-around player? InECCV, pages 216–233

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.532469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f03881bb-577e-44d7-8196-ef4ba1862f66 · outbound

This paper cites Seed-bench: Benchmarking multimodal large language models.

X2SAM: Any Segmentation in Images and Videos Seed-bench: Benchmarking multimodal large language models

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.584571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5fdedad0-1ec3-4043-96e2-c902833b1ce8 · outbound

This paper cites Evaluating Object Hallucination in Large Vision-Language Models.

X2SAM: Any Segmentation in Images and Videos Evaluating Object Hallucination in Large Vision-Language Models

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-05-11T15:01:05.291358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9068ae36-3707-43b3-ac8a-39d266f118ab · outbound

This paper cites A diagram is worth a dozen images.

X2SAM: Any Segmentation in Images and Videos A diagram is worth a dozen images

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.590763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d7dc8b0b-ed25-4f49-90b6-46dbdaae1da6 · outbound

This paper cites Microsoft coco: Common objects in context.

X2SAM: Any Segmentation in Images and Videos Microsoft coco: Common objects in context

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.574063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 01815b34-207c-46b7-a5d7-16668649f050 · outbound

This paper cites Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis.

X2SAM: Any Segmentation in Images and Videos Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.566380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 671e29d7-b81b-4d47-b747-e2a39e9f0744 · outbound

This paper cites Mvbench: A comprehensive multi-modal video understanding benchmark.

X2SAM: Any Segmentation in Images and Videos Mvbench: A comprehensive multi-modal video understanding benchmark

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.570596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9f13f2ab-6e5e-450e-92a2-663be295ff92 · outbound

This paper cites Mlvu: Benchmarking multi-task long video understanding.

X2SAM: Any Segmentation in Images and Videos Mlvu: Benchmarking multi-task long video understanding

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.577708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 85d37fd3-c47c-45a1-8d4b-29d6ce4dc423 · outbound

This paper cites Longvideobench: A benchmark for long-context interleaved video-language understanding.NeurIPS, 37:28828–28857.

X2SAM: Any Segmentation in Images and Videos Longvideobench: A benchmark for long-context interleaved video-language understanding.NeurIPS, 37:28828–28857

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.594663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 03bffbe7-5c54-49a6-8635-8d9a376f551f · outbound

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

X2SAM: Any Segmentation in Images and Videos Masked-attention mask transformer for universal image segmentation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.608794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0d3a54b7-25d4-4926-8063-01edbde880b2 · outbound

This paper cites Cris: Clip-driven referring image segmentation.

X2SAM: Any Segmentation in Images and Videos Cris: Clip-driven referring image segmentation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.562547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 94abc434-0dfc-4bc3-ace5-c915712e418f · outbound

This paper cites PG-Video-LLaVA: Pixel Grounding Large Video-Language Models.

X2SAM: Any Segmentation in Images and Videos PG-Video-LLaVA: Pixel Grounding Large Video-Language Models

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:01:05.448353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a2142cf6-256c-4bea-b154-a585cc137bd9 · outbound

This paper cites Videochat: Chat-centric video understanding.Science China Information Sciences, 68(10):200102.

X2SAM: Any Segmentation in Images and Videos Videochat: Chat-centric video understanding.Science China Information Sciences, 68(10):200102

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.552357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7feb229b-18d6-4698-89c7-3d85fe25ea84 · outbound

This paper cites Chat-univi: Unified visual representation empowers large language models with image and video understanding.

X2SAM: Any Segmentation in Images and Videos Chat-univi: Unified visual representation empowers large language models with image and video understanding

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.543460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 167756bb-13c3-4fc1-b365-064c3411a38c · outbound

This paper cites Vila: On pre-training for visual language models.

X2SAM: Any Segmentation in Images and Videos Vila: On pre-training for visual language models

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:29:12.547625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Reference resolution

Outbound references

Pith citing papers