VUDG: A Dataset for Video Understanding Domain Generalization | Paper Citation Record

Observation 2ac25e71-3ae8-449b-9bd8-4267519269bd · outbound

This paper cites Mm-vit: Multi-modal video transformer for compressed video action recognition.

VUDG: A Dataset for Video Understanding Domain Generalization Mm-vit: Multi-modal video transformer for compressed video action recognition

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:18.898598Z

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Observation 408e9a2d-3b76-43e1-8d19-c0db8a1f8b40 · outbound

This paper cites Mar: Masked autoencoders for efficient action recognition.IEEE Transactions on Multimedia, 26:218–233, 2023.

VUDG: A Dataset for Video Understanding Domain Generalization Mar: Masked autoencoders for efficient action recognition.IEEE Transactions on Multimedia, 26:218–233, 2023

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:18.765665Z

Source-reported events for the cited work

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Observation d3ca7c93-d783-4fc9-8c8f-d91f16364647 · outbound

This paper cites Mnv3-mfae: A lightweight network for video action recognition.Electronics, 14(5):981, 2025.

VUDG: A Dataset for Video Understanding Domain Generalization Mnv3-mfae: A lightweight network for video action recognition.Electronics, 14(5):981, 2025

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:18.544716Z

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Observation 0f4a7285-0452-42b3-a448-a88371dc3454 · outbound

This paper cites Swinbert: End-to-end transformers with sparse attention for video captioning.

VUDG: A Dataset for Video Understanding Domain Generalization Swinbert: End-to-end transformers with sparse attention for video captioning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:18.266711Z

Source-reported events for the cited work

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Observation d18ed918-683d-4e62-9802-8732f95e455e · outbound

This paper cites End-to-end generative pretraining for multimodal video captioning.

VUDG: A Dataset for Video Understanding Domain Generalization End-to-end generative pretraining for multimodal video captioning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:18.135728Z

Source-reported events for the cited work

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Observation 2dd63dcc-8a37-4eba-92da-5b710be2637a · outbound

This paper cites Text with knowledge graph augmented transformer for video captioning.

VUDG: A Dataset for Video Understanding Domain Generalization Text with knowledge graph augmented transformer for video captioning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:17.924672Z

Source-reported events for the cited work

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Observation 3c44d19c-02c3-41b2-b73a-b3873a620ef5 · outbound

This paper cites Automatic video captioning using tree hierarchical deep convolutional neural network and asrnn-bi-directional lstm.Computing, 106(11):3691–3709, 2024.

VUDG: A Dataset for Video Understanding Domain Generalization Automatic video captioning using tree hierarchical deep convolutional neural network and asrnn-bi-directional lstm.Computing, 106(11):3691–3709, 2024

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:17.778516Z

Source-reported events for the cited work

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Observation da4843da-867d-484e-a70b-f61e44a77040 · outbound

This paper cites Invariant grounding for video question answering.

VUDG: A Dataset for Video Understanding Domain Generalization Invariant grounding for video question answering

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:17.551486Z

Source-reported events for the cited work

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Observation a94c8fd1-48a4-4d3b-b56e-525276fc14cb · outbound

This paper cites From representation to reasoning: Towards both evidence and commonsense reasoning for video question-answering.

VUDG: A Dataset for Video Understanding Domain Generalization From representation to reasoning: Towards both evidence and commonsense reasoning for video question-answering

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:17.195284Z

Source-reported events for the cited work

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Observation 39c95ccd-7480-4c1c-947a-a64ad30d46af · outbound

This paper cites Morevqa: Exploring modular reasoning models for video question answering.

VUDG: A Dataset for Video Understanding Domain Generalization Morevqa: Exploring modular reasoning models for video question answering

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:09.991658Z

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Observation 719fcd8c-da12-49b7-952c-787b07e52646 · outbound

This paper cites Diversifying spatial-temporal perception for video domain generalization.Advances in Neural Information Processing Systems, 36:56012–56026, 2023.

VUDG: A Dataset for Video Understanding Domain Generalization Diversifying spatial-temporal perception for video domain generalization.Advances in Neural Information Processing Systems, 36:56012–56026, 2023

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:16.929225Z

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Observation c6ae223e-649a-476b-b129-640cd0b54210 · outbound

This paper cites A multi-modal egocentric activity recognition approach towards video domain generalization.Sensors, 24(8):2491, 2024.

VUDG: A Dataset for Video Understanding Domain Generalization A multi-modal egocentric activity recognition approach towards video domain generalization.Sensors, 24(8):2491, 2024

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:10.172317Z

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Observation 9aa4dada-0a55-4abf-a65a-93b597e49f3f · outbound

This paper cites Meta-causal learning for single domain generalization.

VUDG: A Dataset for Video Understanding Domain Generalization Meta-causal learning for single domain generalization

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:16.764934Z

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Observation 5de17fab-d8c3-44db-9614-aade6df7d16b · outbound

This paper cites Tgif-qa: Toward spatio-temporal reasoning in visual question answering.

VUDG: A Dataset for Video Understanding Domain Generalization Tgif-qa: Toward spatio-temporal reasoning in visual question answering

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:16.645225Z

Source-reported events for the cited work

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Observation 3642f322-b4d7-420c-99d3-6c0c4b4e89ee · outbound

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

VUDG: A Dataset for Video Understanding Domain Generalization Mvbench: A comprehensive multi-modal video understanding benchmark

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:10.406120Z

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Observation 408e7f61-f69b-46cd-97ec-da0311486d57 · outbound

This paper cites VideoVista: A Versatile Benchmark for Video Understanding and Reasoning.

VUDG: A Dataset for Video Understanding Domain Generalization VideoVista: A Versatile Benchmark for Video Understanding and Reasoning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:10.535033Z

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Observation 4d7565d3-43ba-40ea-bf00-649e69218ad5 · outbound

This paper cites Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis.

VUDG: A Dataset for Video Understanding Domain Generalization Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:10.659981Z

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Observation 04193e7e-8181-459f-9af9-e29e13903829 · outbound

This paper cites Vatex: A large-scale, high-quality multilingual dataset for video-and-language research.

VUDG: A Dataset for Video Understanding Domain Generalization Vatex: A large-scale, high-quality multilingual dataset for video-and-language research

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:16.532575Z

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Observation bfef86a0-5c3d-43d5-acfa-ccba42c555b1 · outbound

This paper cites Multimodal Datasets and Benchmarks for Reasoning about Dynamic Spatio-Temporality in Everyday Environments.

VUDG: A Dataset for Video Understanding Domain Generalization Multimodal Datasets and Benchmarks for Reasoning about Dynamic Spatio-Temporality in Everyday Environments

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:32:14.306658Z

Source-reported events for the cited work

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Observation f001e191-3bef-465f-873e-5f0544e86273 · outbound

This paper cites Internvid: A large-scale video-text dataset for multimodal understanding and generation.

VUDG: A Dataset for Video Understanding Domain Generalization Internvid: A large-scale video-text dataset for multimodal understanding and generation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:16.400235Z

Source-reported events for the cited work

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Observation ca3c054d-30ba-46a5-a16e-d55863fa1781 · outbound

This paper cites Sharegpt4video: Improving video understanding and generation with better captions.Advances in Neural Information Processing Systems, 37:19472– 19495, 2024.

VUDG: A Dataset for Video Understanding Domain Generalization Sharegpt4video: Improving video understanding and generation with better captions.Advances in Neural Information Processing Systems, 37:19472– 19495, 2024

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:11.012551Z

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Observation aa8fcc48-396c-45eb-8395-952d6e62d9e5 · outbound

This paper cites Qwen2.5-VL Technical Report.

VUDG: A Dataset for Video Understanding Domain Generalization Qwen2.5-VL Technical Report

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:11.135409Z

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Observation 47e1aad9-c1a1-46fb-9736-104483cb5b61 · outbound

This paper cites Activitynet: A large-scale video benchmark for human activity understanding.

VUDG: A Dataset for Video Understanding Domain Generalization Activitynet: A large-scale video benchmark for human activity understanding

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:11.277723Z

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Observation 0f30c54c-a707-413a-acfd-957ef53fdc5a · outbound

This paper cites The Kinetics Human Action Video Dataset.

VUDG: A Dataset for Video Understanding Domain Generalization The Kinetics Human Action Video Dataset

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:11.404740Z

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Observation 12e75b38-7b5e-4f43-91bf-3494471c519f · outbound

This paper cites Hollywood in homes: Crowdsourcing data collection for activity understanding.

VUDG: A Dataset for Video Understanding Domain Generalization Hollywood in homes: Crowdsourcing data collection for activity understanding

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:11.537568Z

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Observation 3daff457-2f87-4fc7-b2e4-9a7c19385452 · outbound

This paper cites TVQA: Localized, Compositional Video Question Answering.

VUDG: A Dataset for Video Understanding Domain Generalization TVQA: Localized, Compositional Video Question Answering

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:11.633968Z

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Observation 3abd516f-781c-4d5d-bd2e-5341b2b2387d · outbound

This paper cites Video question answering via gradually refined attention over appearance and motion.

VUDG: A Dataset for Video Understanding Domain Generalization Video question answering via gradually refined attention over appearance and motion

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:11.713809Z

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Observation fe10182d-2607-4e6b-a8cc-aa468c18279e · outbound

This paper cites Msr-vtt: A large video description dataset for bridging video and language.

VUDG: A Dataset for Video Understanding Domain Generalization Msr-vtt: A large video description dataset for bridging video and language

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:11.808333Z

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Observation a479c01a-fbd6-4be4-90ed-34515963f71f · outbound

This paper cites Domain generalization for video anomaly detection considering diverse anomaly types.Signal, Image and Video Processing, 18(4):3691–3704, 2024.

VUDG: A Dataset for Video Understanding Domain Generalization Domain generalization for video anomaly detection considering diverse anomaly types.Signal, Image and Video Processing, 18(4):3691–3704, 2024

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:16.181783Z

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Observation afd06ccc-503f-4e77-90a8-fad64cd4d8f5 · outbound

This paper cites Video-audio domain generalization via confounder disentanglement.

VUDG: A Dataset for Video Understanding Domain Generalization Video-audio domain generalization via confounder disentanglement

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:16.076518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e8ae2171-134a-421e-a12a-96bcb2751ee4 · outbound

This paper cites Videodg: General- izing temporal relations in videos to novel domains.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11):7989–8004, 2021.

VUDG: A Dataset for Video Understanding Domain Generalization Videodg: General- izing temporal relations in videos to novel domains.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11):7989–8004, 2021

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:15.938790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 83944137-ba67-43fa-80a5-680fe3cc3151 · outbound

This paper cites Ani-gifs: A benchmark dataset for domain generalization of action recognition from gifs.Frontiers in Computer Science, 4:876846, 2022.

VUDG: A Dataset for Video Understanding Domain Generalization Ani-gifs: A benchmark dataset for domain generalization of action recognition from gifs.Frontiers in Computer Science, 4:876846, 2022

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:15.819775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 88bec4d3-31fa-4d79-bf07-42b717047a3a · outbound

This paper cites What can a cook in italy teach a mechanic in india? action recognition generalisation over scenarios and locations.

VUDG: A Dataset for Video Understanding Domain Generalization What can a cook in italy teach a mechanic in india? action recognition generalisation over scenarios and locations

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:15.731642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3173452b-aaee-44b7-8dec-102394f24a74 · outbound

This paper cites Ego4d: Around the world in 3,000 hours of egocentric video.

VUDG: A Dataset for Video Understanding Domain Generalization Ego4d: Around the world in 3,000 hours of egocentric video

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:12.349303Z

Source-reported events for the cited work

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Observation 50943904-c01d-455a-9c31-7a1f84084fe4 · outbound

This paper cites Multimodal motion conditioned diffusion model for skeleton- based video anomaly detection.

VUDG: A Dataset for Video Understanding Domain Generalization Multimodal motion conditioned diffusion model for skeleton- based video anomaly detection

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:15.579291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ea7fe981-ba3d-46fc-b507-43ec8e51e7b8 · outbound

This paper cites Activitynet-qa: A dataset for understanding complex web videos via question answering.

VUDG: A Dataset for Video Understanding Domain Generalization Activitynet-qa: A dataset for understanding complex web videos via question answering

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:12.589851Z

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Observation 1acd7fa0-0319-4d83-a723-8ff7b95ac313 · outbound

This paper cites Egoschema: A diagnostic benchmark for very long-form video language understanding.Advances in Neural Information Processing Systems, 36:46212–46244, 2023.

VUDG: A Dataset for Video Understanding Domain Generalization Egoschema: A diagnostic benchmark for very long-form video language understanding.Advances in Neural Information Processing Systems, 36:46212–46244, 2023

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:12.660743Z

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Observation dec74c2f-daa6-4af3-993d-154d4e839773 · outbound

This paper cites Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models.

VUDG: A Dataset for Video Understanding Domain Generalization Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:12.770007Z

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Observation 57437514-aca4-4203-9fdb-6bd9ed8012ea · outbound

This paper cites TempCompass: Do Video LLMs Really Understand Videos?.

VUDG: A Dataset for Video Understanding Domain Generalization TempCompass: Do Video LLMs Really Understand Videos?

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:12.883906Z

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Observation e93b29a3-6889-478a-9775-24288b4fe2fe · outbound

This paper cites Video-chatgpt: Towards detailed video understanding via large vision and language models, 2024.

VUDG: A Dataset for Video Understanding Domain Generalization Video-chatgpt: Towards detailed video understanding via large vision and language models, 2024

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:12.990057Z

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Observation b0e01d63-607a-4dd7-86f7-e68e3e54acb9 · outbound

This paper cites Poem: polarization of embeddings for domain-invariant representations.

VUDG: A Dataset for Video Understanding Domain Generalization Poem: polarization of embeddings for domain-invariant representations

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:15.456435Z

Source-reported events for the cited work

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Observation de7d5e04-b281-46b1-8e1c-6643da3feda6 · outbound

This paper cites Video-text as game players: Hierarchical banzhaf interaction for cross-modal representation learning.

VUDG: A Dataset for Video Understanding Domain Generalization Video-text as game players: Hierarchical banzhaf interaction for cross-modal representation learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:15.297134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f3122d34-d08f-4471-b2fb-b56761562758 · outbound

This paper cites Clifton, and Jie Chen.

VUDG: A Dataset for Video Understanding Domain Generalization Clifton, and Jie Chen

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:15.126826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 88d004b5-88ee-426f-90fa-505684e4b023 · outbound

This paper cites VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs.

VUDG: A Dataset for Video Understanding Domain Generalization VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:13.326003Z

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Observation b324e589-68a1-4714-8d08-3deb4559a04d · outbound

This paper cites MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens.

VUDG: A Dataset for Video Understanding Domain Generalization MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:13.398642Z

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Observation 7ddc9ed9-fd2d-4530-ba5f-289c8eae72c8 · outbound

This paper cites VideoChat: Chat-Centric Video Understanding.

VUDG: A Dataset for Video Understanding Domain Generalization VideoChat: Chat-Centric Video Understanding

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:13.451229Z

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Observation 52315005-d983-45bb-afc4-a0374560c0ed · outbound

This paper cites Video-LLaVA: Learning United Visual Representation by Alignment Before Projection.

VUDG: A Dataset for Video Understanding Domain Generalization Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:13.505372Z

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Observation f53ff2bb-efdb-4858-b175-7b01a95b1168 · outbound

This paper cites mPLUG-owl3: Towards long image-sequence understanding in multi-modal large language models.

VUDG: A Dataset for Video Understanding Domain Generalization mPLUG-owl3: Towards long image-sequence understanding in multi-modal large language models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:14.916081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 56e612ab-1b36-4961-b76d-e4097da982a5 · outbound

This paper cites Video-ccam: Enhancing video-language understanding with causal cross-attention masks for short and long videos, 2024.

VUDG: A Dataset for Video Understanding Domain Generalization Video-ccam: Enhancing video-language understanding with causal cross-attention masks for short and long videos, 2024

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:32:14.721883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e5057a45-94c8-41c0-9308-1e4fcef91d8a · outbound

This paper cites Videollama 3: Frontier multimodal foundation models for image and video understanding, 2025.

VUDG: A Dataset for Video Understanding Domain Generalization Videollama 3: Frontier multimodal foundation models for image and video understanding, 2025

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:13.692363Z

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Observation 1cd6c106-ca4c-4be1-9780-1baf1543045a · outbound

This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022.

VUDG: A Dataset for Video Understanding Domain Generalization Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:13.775272Z

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Observation 40ee116f-83bc-4a1e-8d60-984fb2908a3f · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

VUDG: A Dataset for Video Understanding Domain Generalization An image is worth 16x16 words: Transformers for image recognition at scale

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T12:32:13.822308Z

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Observation b7f291b6-cb27-4868-aac0-081effaf01d9 · outbound

This paper cites C- pack: Packed resources for general chinese embeddings.

VUDG: A Dataset for Video Understanding Domain Generalization C- pack: Packed resources for general chinese embeddings

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