Paper Citation Record · LEDGER

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models

As of 20 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 7 inbound Pith citation observations for arXiv:2505.12589.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.12589 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:35:54.610338Z

measured 69 of 69 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:15:47.263691Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-29T22:44:01.321727Z

Reference resolution

62 of 62 outbound references displayed

verified exact0
verified fuzzy36
unresolved26
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fffc2ec5-bd33-452b-84d3-763e3051b91e · outbound

This paper cites Robust real-time unusual event detection using multiple fixed-location monitors.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Robust real-time unusual event detection using multiple fixed-location monitors

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.603806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7cdaba83-6491-4828-b94e-3b4772471824 · outbound

This paper cites Col- laborative learning of anomalies with privacy (clap) for unsupervised video anomaly detection: A new baseline.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Col- laborative learning of anomalies with privacy (clap) for unsupervised video anomaly detection: A new baseline

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.588574Z

Source-reported events for the cited work

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Observation bf5b81ce-ba1d-4dc2-9024-f2c810c0daf0 · outbound

This paper cites Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.572276Z

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Observation 2d21df78-cea7-458f-bc59-e64033008ec5 · outbound

This paper cites Qwen2.5-VL Technical Report.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Qwen2.5-VL Technical Report

Reference 4

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Observation 872cfcbe-b287-4059-bdfd-480ccac673a6 · outbound

This paper cites A new benchmark dataset for semi-supervised video anomaly detection and anticipation in complex scenes.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models A new benchmark dataset for semi-supervised video anomaly detection and anticipation in complex scenes

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.556566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c8f70ae5-f7ad-4e0f-8d2d-fbd3975a9b5c · outbound

This paper cites A Benchmark for Crime Surveillance Video Analysis with Large Models.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models A Benchmark for Crime Surveillance Video Analysis with Large Models

Reference 6

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Source-reported events for the cited work

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Observation 3637e468-1fd4-4c26-a45d-a4c56628d4b6 · outbound

This paper cites PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action Chain.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action Chain

Reference 7

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Source-reported events for the cited work

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Observation 3a20c8e5-9658-4394-8b3d-e13cbc5bd47c · outbound

This paper cites ShareGPT4Video: Improving Video Understanding and Generation with Better Captions.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models ShareGPT4Video: Improving Video Understanding and Generation with Better Captions

Reference 8

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Source-reported events for the cited work

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Observation 32c19a5f-1b8a-4617-8919-70bbadeae1ae · outbound

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

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 9

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Source-reported events for the cited work

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Observation c79afe55-5a95-4e55-9a4e-eef1245e05b3 · outbound

This paper cites Meva: A large-scale multiview, multimodal video dataset for activity detection.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Meva: A large-scale multiview, multimodal video dataset for activity detection

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.541965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7684a559-1945-4c3e-8832-c2b8d34ee97f · outbound

This paper cites A survey on multimodal large language models for autonomous driving.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models A survey on multimodal large language models for autonomous driving

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.527010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 50243912-abb3-4ea7-a11b-c8d22706a5d7 · outbound

This paper cites Any-shot sequential anomaly detection in surveillance videos.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Any-shot sequential anomaly detection in surveillance videos

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.511792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b05d6f78-5e5f-4c83-9fe6-b3e44bc82297 · outbound

This paper cites MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding

Reference 13

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Source-reported events for the cited work

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Observation b7f6d8a5-763d-4ad0-8b3a-9d7f1d421039 · outbound

This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 14

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Source-reported events for the cited work

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Observation 441499e3-c931-4ace-b567-22ca5bfcc405 · outbound

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

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

Reference 15

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Source-reported events for the cited work

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Observation 751cdc9f-2f7b-4700-9239-60058aaf2876 · outbound

This paper cites Chatglm: A family of large language models from glm-130b to glm-4 all tools, 2024.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Chatglm: A family of large language models from glm-130b to glm-4 all tools, 2024

Reference 16

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Source-reported events for the cited work

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Observation df00b970-8667-45ef-b1e6-a5e22a608ddf · outbound

This paper cites Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:54.408983Z

Source-reported events for the cited work

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Observation 4dcd1813-98bb-4a7c-9d07-376288f0226a · outbound

This paper cites Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models

Reference 18

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Source-reported events for the cited work

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Observation 72ced7d9-32e8-4287-881c-afd8a511e650 · outbound

This paper cites Smart city as a smart service system: Human-computer interaction and smart city surveillance systems.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Smart city as a smart service system: Human-computer interaction and smart city surveillance systems

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.487316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3c71c165-dea2-407a-acf1-83244904fbeb · outbound

This paper cites Visual question answering: A survey of methods, datasets, evaluation, and challenges.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Visual question answering: A survey of methods, datasets, evaluation, and challenges

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.472017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 93c585a4-5ece-44f2-938b-37d03c7f1fe6 · outbound

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SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Unresolved cited work

Reference 21

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Source-reported events for the cited work

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Observation 458fdf29-7cf1-49fe-bee5-6d48478212ea · outbound

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

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models LLaVA-OneVision: Easy Visual Task Transfer

Reference 22

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

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Seed-bench: Benchmarking multimodal large language models

Reference 23

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Observation 6bff518f-a5ba-4fb6-8921-1ec9cde4f3ad · outbound

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SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models A Survey on Benchmarks of Multimodal Large Language Models

Reference 24

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Source-reported events for the cited work

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Observation 19e2cc2b-dcdc-4f50-ac31-f8742995830e · outbound

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

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Mvbench: A comprehensive multi-modal video understanding benchmark

Reference 25

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Source-reported events for the cited work

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Observation de62ac9d-ae02-49fc-9b21-f813fa3989da · outbound

This paper cites Anomaly detection and localization in crowded scenes.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Anomaly detection and localization in crowded scenes

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.425019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 966a1e2a-11cb-49eb-ba76-deda696ce2ee · outbound

This paper cites A survey of state of the art large vision language models: Alignment, benchmark, evaluations and challenges.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models A survey of state of the art large vision language models: Alignment, benchmark, evaluations and challenges

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.410065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cbd70aa2-e512-4f34-9072-c2a7612c2f7a · outbound

This paper cites Revealing spatio-temporal evolution of urban visual environments with street view imagery.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Revealing spatio-temporal evolution of urban visual environments with street view imagery

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.394176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b69cc2d9-d977-4da0-bb2d-6b4248a6a762 · outbound

This paper cites A case for distributed multilevel storage infrastructure for visual surveillance in intelligent transportation networks.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models A case for distributed multilevel storage infrastructure for visual surveillance in intelligent transportation networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.379501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0b2df4de-ff4b-4be6-86bd-f90b0a3e9ece · outbound

This paper cites Mm-safetybench: A benchmark for safety evaluation of multimodal large language models.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Mm-safetybench: A benchmark for safety evaluation of multimodal large language models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.364280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2055165c-aa15-4c86-9c9d-dbb29ba1c03a · outbound

This paper cites Liu, Dingkang Yang, Yan Wang, Jing Liu, and Liang Song.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Liu, Dingkang Yang, Yan Wang, Jing Liu, and Liang Song

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.349726Z

Source-reported events for the cited work

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Observation 1afb5bc6-715b-4bfd-8e8b-8bbe36f872c7 · outbound

This paper cites Generalized video anomaly event detection: Systematic taxonomy and comparison of deep models.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Generalized video anomaly event detection: Systematic taxonomy and comparison of deep models

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.335099Z

Source-reported events for the cited work

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Observation 72abb966-3b1a-4657-b69d-c7dbc5246c68 · outbound

This paper cites Mmbench: Is your multi-modal model an all-around player? In European conference on computer vision, pages 216–233.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Mmbench: Is your multi-modal model an all-around player? In European conference on computer vision, pages 216–233

Reference 33

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Observation 39187d1d-2899-4278-94ab-f53c03e634df · outbound

This paper cites Abnormal event detection at 150 fps in matlab.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Abnormal event detection at 150 fps in matlab

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.311435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 35045b39-f3d0-4d7e-b990-49b91e6818e2 · outbound

This paper cites A revisit of sparse coding based anomaly detection in stacked rnn framework.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models A revisit of sparse coding based anomaly detection in stacked rnn framework

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.297474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 93891901-1c52-4eba-ae0c-293f280fc4d4 · outbound

This paper cites Data sets, modeling, and decision making in smart cities: A survey.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Data sets, modeling, and decision making in smart cities: A survey

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.283576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a2f5efdf-269e-461b-a8b9-db50c14e8818 · outbound

This paper cites Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models

Reference 37

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This paper cites VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:54.504822Z

Source-reported events for the cited work

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Observation 058c3887-9ac9-41f1-98a1-5eb098542237 · outbound

This paper cites Spatiotemporal anomaly detection using deep learning for real-time video surveillance.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Spatiotemporal anomaly detection using deep learning for real-time video surveillance

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.270103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 93471766-3d1a-4cff-a46d-f969a82a7f7c · outbound

This paper cites A comprehensive analysis of real-time video anomaly detection methods for human and vehicular movement.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models A comprehensive analysis of real-time video anomaly detection methods for human and vehicular movement

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.255406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9044befd-ecbe-4b6f-9cd3-39cd3421c0b7 · outbound

This paper cites Deep learning approaches for video-based anomalous activity detection.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Deep learning approaches for video-based anomalous activity detection

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.241210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 984c4a15-2edf-45e2-8fcd-6ae83989f3a3 · outbound

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SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Sreenu and M

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.226627Z

Source-reported events for the cited work

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Observation 0301d947-9ba1-43b4-aee3-f3e614732e5f · outbound

This paper cites Real-world anomaly detection in surveillance videos.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Real-world anomaly detection in surveillance videos

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.211797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 22c5a9c3-0b60-4b6d-b098-63f62eb8d450 · outbound

This paper cites Video surveillance systems-current status and future trends.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Video surveillance systems-current status and future trends

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.197674Z

Source-reported events for the cited work

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Observation c6e80720-ec20-467b-bc97-974b2c9d474e · outbound

This paper cites Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:54.535439Z

Source-reported events for the cited work

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Observation c0a856c2-9cd8-4e45-84f9-e27d9c14ff99 · outbound

This paper cites Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:54.540185Z

Source-reported events for the cited work

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Observation 530bff89-0940-4346-be89-1238686f1c15 · outbound

This paper cites Longvlm: Efficient long video understanding via large language models.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Longvlm: Efficient long video understanding via large language models

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.183761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cf851d3b-a9d3-4cdd-b709-32609fc23f28 · outbound

This paper cites Open-vocabulary video anomaly detection.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Open-vocabulary video anomaly detection

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.169057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bb2a0f43-1aa0-480a-b3db-3a4f28a86858 · outbound

This paper cites Next-qa: Next phase of question- answering to explaining temporal actions.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Next-qa: Next phase of question- answering to explaining temporal actions

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.154028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation aaa5798e-b23e-451a-9175-d945872969d5 · outbound

This paper cites Funqa: Towards surprising video comprehension.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Funqa: Towards surprising video comprehension

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.139360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b5f1bb64-ec22-434c-990f-6dad8a9b7069 · outbound

This paper cites Video structured description technology based intelligence analysis of surveillance videos for public security applications.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Video structured description technology based intelligence analysis of surveillance videos for public security applications

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.125668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bc85f8e4-ca3d-4284-be05-3d32b96b39f1 · outbound

This paper cites Qwen2.5 Technical Report.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Qwen2.5 Technical Report

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:54.566643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de00a680-c873-4025-903c-fda999fe2241 · outbound

This paper cites Svbench: A benchmark with temporal multi-turn dialogues for streaming video understanding.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Svbench: A benchmark with temporal multi-turn dialogues for streaming video understanding

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:54.571037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8c32b1e-3de4-436f-ae63-f2edb5426ee5 · outbound

This paper cites MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:54.575145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d622a28d-c4c1-44e6-8dda-9fcb9ef1e7c2 · outbound

This paper cites Surveillance video-and-language understanding: from small to large multimodal models.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Surveillance video-and-language understanding: from small to large multimodal models

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.110627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 70340733-8589-4245-8597-4c503cbd8d26 · outbound

This paper cites Towards surveillance video-and-language understanding: New dataset baselines and challenges.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Towards surveillance video-and-language understanding: New dataset baselines and challenges

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.094492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b083faaf-25b0-4dcd-94d6-7b2fbd5ce6c4 · outbound

This paper cites Har- nessing large language models for training-free video anomaly detection.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Har- nessing large language models for training-free video anomaly detection

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.079581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c421cddf-bb31-478e-a1b2-e2bf9fc59f63 · outbound

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

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:54.592357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d21578a-3594-46d0-a7ff-78af9574f860 · outbound

This paper cites Llava-next: A strong zero-shot video understanding model, April 2024.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Llava-next: A strong zero-shot video understanding model, April 2024

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.063966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c50dad0f-113a-4b9e-be24-b35dbb991139 · outbound

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

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models LLaVA-Video: Video Instruction Tuning With Synthetic Data

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T20:35:54.601567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4eba873f-5cad-4182-9561-273a4df7b66b · outbound

This paper cites Anomalynet: An anomaly detection network for video surveillance.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models Anomalynet: An anomaly detection network for video surveillance

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.048972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7632f66e-b6dd-4a48-954d-ff2c0ecde234 · outbound

This paper cites 2 0 1 8 -0 3 -1 5 _ 1 0 _ 1 3 1.

SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models 2 0 1 8 -0 3 -1 5 _ 1 0 _ 1 3 1

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:35:55.032878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 4b115999-15e5-4575-8c98-d5ceb4a12853 · inbound

STER-VLM: Spatio-Temporal With Enhanced Reference Vision-Language Models cites this paper.

STER-VLM: Spatio-Temporal With Enhanced Reference Vision-Language Models SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T19:06:23.701939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 239e5431-98ec-4e19-9a00-164b5ca3ca63 · inbound

ProcObject-10K: Benchmarking Object-Centric Procedural Understanding in Instructional Videos cites this paper.

ProcObject-10K: Benchmarking Object-Centric Procedural Understanding in Instructional Videos SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-17T03:08:56.600848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b1c66789-5806-49e3-8774-61e562d6ebc8 · inbound

CrashSight: A Phase-Aware, Infrastructure-Centric Video Benchmark for Traffic Crash Scene Understanding and Reasoning cites this paper.

CrashSight: A Phase-Aware, Infrastructure-Centric Video Benchmark for Traffic Crash Scene Understanding and Reasoning SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:30:57.908140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3eb130d2-a3de-47cf-ae0d-9389acf53a4b · inbound

MAVEN: A Multi-stage Agentic Annotation Pipeline for Video Reasoning Tasks cites this paper.

MAVEN: A Multi-stage Agentic Annotation Pipeline for Video Reasoning Tasks SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:36:13.989290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 29ab977d-45d4-4862-a0a2-88ef6011c0f5 · inbound

MAVEN: A Multi-stage Agentic Annotation Pipeline for Video Reasoning Tasks cites this paper.

MAVEN: A Multi-stage Agentic Annotation Pipeline for Video Reasoning Tasks SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-12T16:17:21.074502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d59e0931-11dc-4425-8c61-dece5328e698 · inbound

MetaphorVU: Towards Metaphorical Video Understanding cites this paper.

MetaphorVU: Towards Metaphorical Video Understanding SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:44:01.323382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 149f5536-42ec-4d19-9965-3ea8ab966e7b · inbound

From Detection to Understanding: TAR and TAR-Bench for Multi-Task Traffic Anomaly Reasoning cites this paper.

From Detection to Understanding: TAR and TAR-Bench for Multi-Task Traffic Anomaly Reasoning SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-14T04:15:47.263691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.