Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding | Paper Citation Record

Observation d39bed59-f4bd-46df-b1b2-9bcfe84c731f · outbound

This paper cites Localizing mo- ments in video with natural language.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Localizing mo- ments in video with natural language

Reference 1

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Observation 14512fa4-c8ed-4176-b7b8-789f16e95ff5 · outbound

This paper cites Lawrence Zitnick, and Devi Parikh.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Lawrence Zitnick, and Devi Parikh

Reference 2

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Observation ae986ee4-75aa-4e6f-ab89-be88cdb5fd34 · outbound

This paper cites Qwen2.5-VL Technical Report.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Qwen2.5-VL Technical Report

Reference 3

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This paper cites One token to seg them all: Language instructed reasoning seg- mentation in videos.Advances in Neural Information Pro- cessing Systems (NeurIPS), 37:6833–6859.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding One token to seg them all: Language instructed reasoning seg- mentation in videos.Advances in Neural Information Pro- cessing Systems (NeurIPS), 37:6833–6859

Reference 4

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Observation 81260634-4bc2-4b18-819a-4d363323e5ca · outbound

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

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding End-to- end object detection with transformers

Reference 5

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This paper cites Chen and William B.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Chen and William B

Reference 6

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Observation 91bdf40b-93f9-4bc6-8bdb-ad0bc3fe7364 · outbound

This paper cites TimeMarker: A Versatile Video-LLM for Long and Short Video Understanding with Superior Temporal Localization Ability.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding TimeMarker: A Versatile Video-LLM for Long and Short Video Understanding with Superior Temporal Localization Ability

Reference 7

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Observation 559cd5a6-674d-4b9a-85c5-f993e80f5adc · outbound

This paper cites Sutrack: Towards simple and unified single object tracking.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Sutrack: Towards simple and unified single object tracking

Reference 8

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Observation 1b18494c-ce0e-40c8-a5a4-ed71a1834bc6 · outbound

This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 9

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Observation 478d5077-302c-4dfc-bc15-5b34516c64e8 · outbound

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

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-05-17T00:58:46.567418Z

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Observation 05787ab9-47af-4202-a225-2e5012a2b0f3 · outbound

This paper cites V-STaR: Benchmarking Video-LLMs on Video Spatio-Temporal Reasoning.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding V-STaR: Benchmarking Video-LLMs on Video Spatio-Temporal Reasoning

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:58:46.542264Z

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Observation ea1b4a1f-8dee-4cb9-af8a-f60352e59c7a · outbound

This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 12

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Observation 605e6d0c-d22d-473b-81fc-5adda7a1f7b7 · outbound

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

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis

Reference 13

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Observation 04a5bd31-04a1-4392-ad73-e48d61960717 · outbound

This paper cites VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:58:46.551714Z

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Observation c70e0ede-58dc-471e-b6dc-af7f568b3217 · outbound

This paper cites Tall: Temporal activity localization via language query.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Tall: Temporal activity localization via language query

Reference 15

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Observation d92ec788-1196-4346-9dd3-47634f82f031 · outbound

This paper cites Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing

Reference 16

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Observation dda6359b-8f55-455f-bebd-98c639fb5b2a · outbound

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

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Ego4d: Around the world in 3,000 hours of egocentric video

Reference 17

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Observation b2deb3ce-b3e1-4338-b375-6b3edea312e9 · outbound

This paper cites Agqa: A benchmark for compositional spatio-temporal reasoning.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Agqa: A benchmark for compositional spatio-temporal reasoning

Reference 18

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Observation b8ba7102-3c03-4912-bf30-b8486ea8f632 · outbound

This paper cites Context-guided spatio-temporal video grounding.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Context-guided spatio-temporal video grounding

Reference 19

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Observation 62678143-30fe-4a46-942c-f04e3065bf57 · outbound

This paper cites Knowing Your Target: Target-Aware Transformer Makes Better Spatio-Temporal Video Grounding.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Knowing Your Target: Target-Aware Transformer Makes Better Spatio-Temporal Video Grounding

Reference 20

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Observation 9f7a8c12-6772-493b-afab-5c4d15ca4c88 · outbound

This paper cites TRACE: Temporal Grounding Video LLM via Causal Event Modeling.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding TRACE: Temporal Grounding Video LLM via Causal Event Modeling

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:58:46.582114Z

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Observation 9cfe936b-f2db-4cb4-9de6-098390cdaf1e · outbound

This paper cites Vtg-llm: Integrating timestamp knowledge into video llms for enhanced video temporal grounding.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Vtg-llm: Integrating timestamp knowledge into video llms for enhanced video temporal grounding

Reference 22

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Observation fa5dab23-0308-4fde-b04b-3d7f4835fb54 · outbound

This paper cites Creating summaries from user videos.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Creating summaries from user videos

Reference 23

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Observation f63be08b-25b4-4032-b6ef-d97e7fbbaa0c · outbound

This paper cites Lora: Low-rank adaptation of large language models.Inter- national Conference on Learning Representations (ICLR), 1 (2):3.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Lora: Low-rank adaptation of large language models.Inter- national Conference on Learning Representations (ICLR), 1 (2):3

Reference 24

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Observation cb000905-2dd9-4bda-ac22-47530f9a5e60 · outbound

This paper cites Vtimellm: Empower llm to grasp video moments.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Vtimellm: Empower llm to grasp video moments

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T01:01:25.338148Z

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Observation e31a8702-54d6-4968-be8d-f0a0d2829b17 · outbound

This paper cites Lita: Language instructed temporal-localization assistant.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Lita: Language instructed temporal-localization assistant

Reference 26

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Observation 289f36d7-8a6d-40d6-9866-7730ef4ab9a7 · outbound

This paper cites GPT-4o System Card.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding GPT-4o System Card

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-17T00:58:46.585131Z

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Observation 9c37f211-c121-497d-be4f-965d2a61ffbb · outbound

This paper cites Embracing con- sistency: A one-stage approach for spatio-temporal video grounding.Advances in Neural Information Processing Sys- tems (NeurIPS), 35.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Embracing con- sistency: A one-stage approach for spatio-temporal video grounding.Advances in Neural Information Processing Sys- tems (NeurIPS), 35

Reference 28

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Observation ad30844a-01db-414c-9e27-a4ba1cf84b14 · outbound

This paper cites Language repository for long video understanding.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Language repository for long video understanding

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T01:01:25.376136Z

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Observation abdc1bd4-909d-4223-b571-bcd3f492cd85 · outbound

This paper cites Referitgame: Referring to objects in pho- tographs of natural scenes.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Referitgame: Referring to objects in pho- tographs of natural scenes

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T01:01:25.386474Z

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Observation c7763273-4139-44aa-95a3-3aef49417473 · outbound

This paper cites Dense-captioning events in videos.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Dense-captioning events in videos

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T01:01:25.322625Z

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Observation d3550782-3e28-4ce4-9811-8939a0bc7ba8 · outbound

This paper cites Berg, and Mohit Bansal.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Berg, and Mohit Bansal

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T01:01:25.391122Z

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Observation f1ed475d-ccd7-4a0d-8357-fa598ae586fe · outbound

This paper cites Detecting moments and highlights in videos via natural language queries.Advances in Neural Information Processing Systems (NeurIPS), 34:11846–11858.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Detecting moments and highlights in videos via natural language queries.Advances in Neural Information Processing Systems (NeurIPS), 34:11846–11858

Reference 33

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Observation 3c75648d-0ef4-49e2-b309-a9a3f18588ce · outbound

This paper cites LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-05-17T00:58:46.618430Z

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Observation 44b6eeb1-b677-4c82-a566-fdfe4a14c356 · outbound

This paper cites Llava-ST: A multimodal large language model for fine-grained spatial- temporal understanding.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Llava-ST: A multimodal large language model for fine-grained spatial- temporal understanding

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T01:01:25.387524Z

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Observation 3c26515f-7644-4eaa-8667-6f0379f61cb5 · outbound

This paper cites Llava-st: A multimodal large language model for fine-grained spatial- temporal understanding.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Llava-st: A multimodal large language model for fine-grained spatial- temporal understanding

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.585448Z

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Observation 4926f75f-0dfc-422e-8ca1-4d41c72e0e5a · outbound

This paper cites VideoChat: Chat-Centric Video Understanding.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding VideoChat: Chat-Centric Video Understanding

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-05-17T00:58:46.588348Z

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Observation dab7d110-72cf-45aa-bde7-ede966d4b50a · outbound

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

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Mvbench: A comprehensive multi-modal video understand- ing benchmark

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T01:01:25.362837Z

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Observation 98d8840d-f323-4d65-b85c-e426373fbeed · outbound

This paper cites Referdino: Referring video object segmentation with visual grounding founda- tions.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Referdino: Referring video object segmentation with visual grounding founda- tions

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.542121Z

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Observation 055628cf-ae76-45e4-b59b-f1189c9bdaf5 · outbound

This paper cites Video-llava: Learning united visual repre- sentation by alignment before projection.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Video-llava: Learning united visual repre- sentation by alignment before projection

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T01:01:25.342329Z

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Observation 18a6556e-e704-41d1-8a9f-e41e1cc07152 · outbound

This paper cites Glus: Global-local reasoning unified into a single large lan- guage model for video segmentation.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Glus: Global-local reasoning unified into a single large lan- guage model for video segmentation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T01:01:25.359537Z

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Observation 73071e62-ca17-42ba-9f8e-93748517271f · outbound

This paper cites Collaborative static and dynamic vision-language streams for spatio-temporal video ground- ing.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Collaborative static and dynamic vision-language streams for spatio-temporal video ground- ing

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.596988Z

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Observation e70e5bf2-40dd-446c-b33f-21eb56bef62c · outbound

This paper cites Grounding DINO: Marrying dino with grounded pre-training for open-set object detection.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Grounding DINO: Marrying dino with grounded pre-training for open-set object detection

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T01:01:25.392999Z

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Observation 5989b7f1-3842-456a-84c9-24deb37c597d · outbound

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

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding TempCompass: Do Video LLMs Really Understand Videos?

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-17T02:46:17.144047Z

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Observation 854c8ae5-24f8-4e62-983c-a1376105faee · outbound

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

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Swin transformer: Hierarchical vision transformer using shifted windows

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.512763Z

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Observation 470f744a-a41c-44e2-9102-190dfa7b92dd · outbound

This paper cites Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:58:46.612461Z

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Observation 3780dad6-ab2a-414b-8f9d-9e81fe75ccdc · outbound

This paper cites Valley: Video Assistant with Large Language model Enhanced abilitY.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Valley: Video Assistant with Large Language model Enhanced abilitY

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:58:46.556812Z

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Observation ad8f6c2f-e4f5-4540-b780-e9d6848fd243 · outbound

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

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Video-chatgpt: Towards detailed video un- derstanding via large vision and language models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T01:01:25.404626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 91e44e5a-74f7-4fa7-8e38-f613afa1264f · outbound

This paper cites Genera- tion and comprehension of unambiguous object descriptions.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Genera- tion and comprehension of unambiguous object descriptions

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.578060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation dc73d32e-0f37-46d9-acae-47524f284c9b · outbound

This paper cites Open-o3 video: Grounded video reasoning with explicit spatio-temporal evidence.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Open-o3 video: Grounded video reasoning with explicit spatio-temporal evidence

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:58:46.538814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6a904aa3-5c33-48a0-a3d9-cf921f28d043 · outbound

This paper cites Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:58:46.574958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f98fbac6-d048-4c40-aa82-6c942f6f8cea · outbound

This paper cites Streaming long video understanding with large language models.Advances in Neu- ral Information Processing Systems (NeurIPS), 37:119336– 119360.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Streaming long video understanding with large language models.Advances in Neu- ral Information Processing Systems (NeurIPS), 37:119336– 119360

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.593181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 24f6e6ea-3ca1-41f3-ac95-1482a9a73e57 · outbound

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

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding SAM 2: Segment Anything in Images and Videos

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-05-17T00:58:46.598362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3e07ad01-53fd-4060-9769-0c15e0ec069f · outbound

This paper cites Ground- ing action descriptions in videos.Transactions of the Associ- ation for Computational Linguistics (TACL), 1:25–36.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Ground- ing action descriptions in videos.Transactions of the Associ- ation for Computational Linguistics (TACL), 1:25–36

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.563954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0bbdfc4e-8885-4c6e-bfd0-55933f105876 · outbound

This paper cites Timechat: A time-sensitive multimodal large language model for long video understanding.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Timechat: A time-sensitive multimodal large language model for long video understanding

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T01:01:25.380110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9e5eac96-d3a7-4977-b03f-6de7d9092c6a · outbound

This paper cites VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-05-17T00:58:46.523998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7ec0f3a3-2e73-4831-8b4c-eec4f2f10c1e · outbound

This paper cites Moviechat: From dense token to sparse memory for long video understanding.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Moviechat: From dense token to sparse memory for long video understanding

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.560268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e85449fd-1b50-4556-9d36-f763e20685f9 · outbound

This paper cites Tvsum: Summarizing web videos using titles.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Tvsum: Summarizing web videos using titles

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.567376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 60eb5f8a-2ab5-4393-8d2f-8521301de291 · outbound

This paper cites STVGBERT: A visual- linguistic transformer based framework for spatio-temporal video grounding.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding STVGBERT: A visual- linguistic transformer based framework for spatio-temporal video grounding

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.574581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation dc50b21c-4e74-489d-9e1e-b41505fdee92 · outbound

This paper cites Human-centric spatio-temporal video grounding with visual transformers.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Human-centric spatio-temporal video grounding with visual transformers

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.554120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 19a5d37e-6486-4a8a-b153-7e6e0d905da5 · outbound

This paper cites Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:58:46.609124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 994323ee-fe0b-49b7-8ad5-1f43387f8019 · outbound

This paper cites SpaceVLLM: Endowing Multimodal Large Language Model with Spatio-Temporal Video Grounding Capability.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding SpaceVLLM: Endowing Multimodal Large Language Model with Spatio-Temporal Video Grounding Capability

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:58:46.621861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 040a74d2-2dee-4802-b188-0c456b63ffee · outbound

This paper cites HawkEye: Training Video-Text LLMs for Grounding Text in Videos.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding HawkEye: Training Video-Text LLMs for Grounding Text in Videos

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:58:46.547114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 39b8fe18-aaaf-4c37-9a40-2fc6a16bc31c · outbound

This paper cites Can i trust your answer? video question answering.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Can i trust your answer? video question answering

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T01:01:25.366458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 26411682-4ebb-4120-b129-3efad12db353 · outbound

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

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Msr-vtt: A large video description dataset for bridging video and language

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.548266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5ce41bf4-68fd-4d56-847f-814f054843f0 · outbound

This paper cites Visa: Reasoning video object segmentation via large lan- guage models.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Visa: Reasoning video object segmentation via large lan- guage models

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.551056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2cd3a306-6a8e-474e-96b0-bb55670525d1 · outbound

This paper cites Task preference optimization: Improving mul- timodal large language models with vision task alignment.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Task preference optimization: Improving mul- timodal large language models with vision task alignment

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.557132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 93b6b99a-001c-48a6-b010-fd366be95e5f · outbound

This paper cites Tubedetr: Spatio-temporal video ground- ing with transformers.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Tubedetr: Spatio-temporal video ground- ing with transformers

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.570612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2192f487-8071-427c-b643-8aa91420d2df · outbound

This paper cites Zero-shot video question answering via frozen bidirectional language models.Advances in Neural Information Processing Systems (NeurIPS).

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Zero-shot video question answering via frozen bidirectional language models.Advances in Neural Information Processing Systems (NeurIPS)

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.581802Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 79f23c7e-a294-4381-830c-ae8e0993b51a · outbound

This paper cites Qwen3 Technical Report.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Qwen3 Technical Report

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-05-17T00:58:46.534178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3487de46-2ea9-4107-9fa9-66e23515b09e · outbound

This paper cites Tenenbaum.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Tenenbaum

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.545045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a8fb2e04-5606-460b-a560-f9ee8b8dc6b6 · outbound

This paper cites Self-chained image-language model for video localization and question answering.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Self-chained image-language model for video localization and question answering

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T01:01:25.394885Z

Source-reported events for the cited work

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Observation 0bf8e50c-39f6-4e84-860c-d0531d8dfc4e · outbound

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

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-05-17T00:58:46.519397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d3f6849d-288b-4d9f-86fd-6668e103d25e · outbound

This paper cites Sigmoid loss for language image pre-training.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Sigmoid loss for language image pre-training

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T01:01:25.345422Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 645d8179-2cb5-4179-9228-8250a797ca45 · outbound

This paper cites VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-05-17T00:58:46.605355Z

Source-reported events for the cited work

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Observation 0b9a1aa6-493e-42f0-baad-440c81e1a141 · outbound

This paper cites A simple llm framework for long-range video question-answering.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding A simple llm framework for long-range video question-answering

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.536188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f4f65383-30d6-4e85-a504-81211bb2062d · outbound

This paper cites Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-05-17T00:58:46.615391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 129f71a8-9fbb-4def-97d4-53c46bd94599 · outbound

This paper cites LLaVA-Video: Video Instruction Tuning With Synthetic Data.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding LLaVA-Video: Video Instruction Tuning With Synthetic Data

Reference 78

Resolution: verified exact; local_arxiv, observed 2026-05-17T00:58:46.601891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 890a4901-07c9-41c8-bfd3-773d0ae1204e · outbound

This paper cites Where does it exist: Spatio-temporal video grounding for multi-form sentences.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Where does it exist: Spatio-temporal video grounding for multi-form sentences

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.539066Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5c190997-ec5f-41ba-b72f-d5d5304883e6 · outbound

This paper cites An Open and Comprehensive Pipeline for Unified Object Grounding and Detection.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding An Open and Comprehensive Pipeline for Unified Object Grounding and Detection

Reference 80

Resolution: verified exact; arxiv_id, observed 2026-05-17T00:58:46.594864Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 733014c9-ed2f-488a-b7dd-faff8d6fd368 · outbound

This paper cites Given textual featuresQand video featuresV, LLMs employ a textualized box decoder to predict a sequence of tokensy 1:L = (y 1.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Given textual featuresQand video featuresV, LLMs employ a textualized box decoder to predict a sequence of tokensy 1:L = (y 1

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.518611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b75207c1-56e7-42a1-9fba-a15237e2014c · outbound

This paper cites <image> Locate the visual content described by the query <query></query> in the image.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding <image> Locate the visual content described by the query <query></query> in the image

Reference 82

Resolution: malformed identifier; raw_fallback, observed 2026-05-17T00:58:47.515615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3dffa583-cbad-45d7-b506-2eaafeef2ccf · outbound

This paper cites What‘sundertheskier’sfeet?.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding What‘sundertheskier’sfeet?

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.522541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 53cd6288-2f35-4907-8d4b-8d88dd258ac2 · outbound

This paper cites Rows marked with*are zero- shot settings.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Rows marked with*are zero- shot settings

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.533023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 791fcf43-268b-41ec-97f0-4e99d341ef3a · outbound

This paper cites 6–11 present additional qualitative results across im- ages and videos.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding 6–11 present additional qualitative results across im- ages and videos

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.509958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2f563edb-f2f8-474d-a3e0-9305b23cfa48 · outbound

This paper cites Consequently, DEViL is trained to emit a single RST and retrieve one tube per query, without explicitly modeling multiple entities or their roles.

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding Consequently, DEViL is trained to emit a single RST and retrieve one tube per query, without explicitly modeling multiple entities or their roles

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T00:58:47.529950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

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