Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges | Paper Citation Record

Observation 7a0a023e-1640-479c-8230-95ecd1fee14f · outbound

This paper cites Traffic monitoring and accident detection at intersections,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Traffic monitoring and accident detection at intersections,

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This paper cites A survey of vision-based trajec- tory learning and analysis for surveillance,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges A survey of vision-based trajec- tory learning and analysis for surveillance,

Reference 2

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This paper cites Trajectory-based anomalous event detection,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Trajectory-based anomalous event detection,

Reference 3

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This paper cites Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections,

Reference 4

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This paper cites Severity of driver injury and vehicle damage in traffic crashes at intersections: a bayesian hierarchical analysis,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Severity of driver injury and vehicle damage in traffic crashes at intersections: a bayesian hierarchical analysis,

Reference 5

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This paper cites Two-stream convolutional networks for action recognition in videos,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Two-stream convolutional networks for action recognition in videos,

Reference 6

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This paper cites Learning spatiotemporal features with 3d convolutional networks,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Learning spatiotemporal features with 3d convolutional networks,

Reference 7

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This paper cites Quo vadis, action recognition? a new model and the kinetics dataset,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Quo vadis, action recognition? a new model and the kinetics dataset,

Reference 8

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This paper cites Slowfast networks for video recognition,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Slowfast networks for video recognition,

Reference 9

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Observation 8ae9d92e-0c55-4c14-b260-63172779b037 · outbound

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

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Real-world anomaly detection in surveillance videos,

Reference 10

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This paper cites Future frame prediction for anomaly detection–a new baseline,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Future frame prediction for anomaly detection–a new baseline,

Reference 11

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This paper cites Learning memory-guided nor- mality for anomaly detection,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Learning memory-guided nor- mality for anomaly detection,

Reference 12

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Observation 8a4ed46c-ec87-4d43-9dbf-1810b13cdf36 · outbound

This paper cites Uniter: Universal image-text representation learning,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Uniter: Universal image-text representation learning,

Reference 13

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Observation 0d915eb2-cc12-4d91-a601-a02dd232d72c · outbound

This paper cites Align before fuse: Vision and language representation learning with momentum distillation,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Align before fuse: Vision and language representation learning with momentum distillation,

Reference 14

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Observation cc6bc847-2d23-4df1-be3b-f5ccf41a3dda · outbound

This paper cites Less is more: Clipbert for video-and-language learning via sparse sampling,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Less is more: Clipbert for video-and-language learning via sparse sampling,

Reference 15

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This paper cites Videoclip: Contrastive pre-training for zero-shot video-text understanding,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Videoclip: Contrastive pre-training for zero-shot video-text understanding,

Reference 16

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This paper cites GPT-4 Technical Report.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges GPT-4 Technical Report

Reference 17

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Observation 2320f2c0-64dc-4ff6-a19c-0b4923c6b4b7 · outbound

This paper cites Flamingo: a visual language model for few-shot learning,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Flamingo: a visual language model for few-shot learning,

Reference 18

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Observation ef9a3f3c-0c3d-4a13-a339-aecce4443c8a · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,

Reference 19

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This paper cites Anomalous video event detection using spatiotemporal context,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Anomalous video event detection using spatiotemporal context,

Reference 20

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Observation 46d4f0b0-2231-4793-95df-c756a1ab9b1c · outbound

This paper cites Detecting anomalies in people’s trajectories using spectral graph analysis,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Detecting anomalies in people’s trajectories using spectral graph analysis,

Reference 21

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Observation 314d5834-1e69-40c3-9d01-9abd50c48051 · outbound

This paper cites Using support vector machine models for crash injury severity analysis,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Using support vector machine models for crash injury severity analysis,

Reference 22

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This paper cites Accident detection system using image processing and mdr,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Accident detection system using image processing and mdr,

Reference 23

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Observation 66facb1f-0cab-46fa-ac98-36c9e68c0d40 · outbound

This paper cites Temporal segment networks: Towards good practices for deep action recognition,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Temporal segment networks: Towards good practices for deep action recognition,

Reference 24

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Observation c6f9de2a-893b-4ad6-a4ce-5c6cf1c02cf2 · outbound

This paper cites Multimodal machine learning: A survey and taxonomy,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Multimodal machine learning: A survey and taxonomy,

Reference 25

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This paper cites Multimodal deep learning,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Multimodal deep learning,

Reference 26

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This paper cites Deep multimodal learning: A survey on recent advances and trends,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Deep multimodal learning: A survey on recent advances and trends,

Reference 27

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This paper cites SimVLM: Simple Visual Language Model Pretraining with Weak Supervision.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges SimVLM: Simple Visual Language Model Pretraining with Weak Supervision

Reference 28

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This paper cites All in one: Exploring unified video-language pre-training,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges All in one: Exploring unified video-language pre-training,

Reference 29

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

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges The cityscapes dataset for semantic urban scene understanding,

Reference 30

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This paper cites nuscenes: A multimodal dataset for autonomous driving,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges nuscenes: A multimodal dataset for autonomous driving,

Reference 31

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This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Are we ready for autonomous driving? the kitti vision benchmark suite,

Reference 32

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This paper cites Edge computing: Vision and challenges,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Edge computing: Vision and challenges,

Reference 33

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This paper cites Edge intelligence: Paving the last mile of artificial intelligence with edge computing,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Edge intelligence: Paving the last mile of artificial intelligence with edge computing,

Reference 34

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This paper cites Videobert: A joint model for video and language representa- tion learning,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Videobert: A joint model for video and language representa- tion learning,

Reference 35

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This paper cites Vivit: A video vision transformer,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Vivit: A video vision transformer,

Reference 36

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This paper cites Videogpt: Video generation using vq-vae and transformers,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Videogpt: Video generation using vq-vae and transformers,

Reference 37

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

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

Reference 38

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Observation 288793db-74eb-4782-b296-5890ae443464 · outbound

This paper cites Improved Baselines with Visual Instruction Tuning.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Improved Baselines with Visual Instruction Tuning

Reference 39

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This paper cites PaLM-E: An Embodied Multimodal Language Model.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges PaLM-E: An Embodied Multimodal Language Model

Reference 40

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Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Gpt-4v(ision) system card,

Reference 41

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This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context,

Reference 42

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Observation b7be538e-2e9e-4368-a47b-acbe008365b6 · outbound

This paper cites Sora: Creating video from text,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Sora: Creating video from text,

Reference 43

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Observation a12d3737-8413-4032-9198-c63c793d755e · outbound

This paper cites Crash: Crash recognition and anticipation system harnessing with context-aware and temporal focus attentions,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Crash: Crash recognition and anticipation system harnessing with context-aware and temporal focus attentions,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:14.782918Z

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Observation f0149b61-9c89-4859-b042-ebcfd31e88a7 · outbound

This paper cites When language and vision meet road safety: leveraging multimodal large language models for video-based traffic accident analysis.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges When language and vision meet road safety: leveraging multimodal large language models for video-based traffic accident analysis

Reference 45

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Observation 4a95e254-d30b-42cf-a757-3dc95da00af2 · outbound

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

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Learning transferable visual models from natural language supervision,

Reference 46

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Observation 17b3a461-14f7-47f8-a8ff-fbd05e9e0b22 · outbound

This paper cites CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval

Reference 47

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Observation 448b17bc-4b82-4f38-aa8e-10cba87233f4 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 48

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Observation abd4cb4e-e83c-44dc-8829-7d0d6ddcc42b · outbound

This paper cites Quick and robust detection of crash events from video streams,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Quick and robust detection of crash events from video streams,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:14.537088Z

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Observation 1de8b4b1-d6cf-4634-9aca-c2428ce4f0ad · outbound

This paper cites A real-time system for detection of road accidents from video streams,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges A real-time system for detection of road accidents from video streams,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:14.256997Z

Source-reported events for the cited work

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Observation 3a3c7fb1-97ac-4f3a-987e-eb14d147d8a6 · outbound

This paper cites Video-based traffic accident detection: A survey,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Video-based traffic accident detection: A survey,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:13.942348Z

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Observation 87610921-fe17-4f72-9711-b5b7c8f55caf · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges LoRA: Low-Rank Adaptation of Large Language Models

Reference 52

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Observation 4ad41515-89e2-4ee8-aa36-f17d7c4f00e8 · outbound

This paper cites Pentagon relation and Biedenharn-Elliott identity.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Pentagon relation and Biedenharn-Elliott identity

Reference 53

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T20:43:10.153649Z

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Observation a45733c2-3731-4332-a915-3102c9cd5cf3 · outbound

This paper cites Harnessing Neuron Stability to Improve DNN Verification.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Harnessing Neuron Stability to Improve DNN Verification

Reference 54

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T20:43:10.016965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 240f0f88-0037-4bec-9b7f-087d9756e0e4 · outbound

This paper cites Partially hyperbolic diffeomorphisms that are center fixing.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Partially hyperbolic diffeomorphisms that are center fixing

Reference 55

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T20:43:09.921329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4956d886-5e93-4272-b982-2d98a7eb761a · outbound

This paper cites On the consistency of relative facts.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges On the consistency of relative facts

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:04.866116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 645beef6-394d-46aa-8ece-d71fc68a0b65 · outbound

This paper cites Leveraging video-llms for crash detection and narrative generation: Performance analysis and challenges,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Leveraging video-llms for crash detection and narrative generation: Performance analysis and challenges,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:13.654406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 35443ac5-fef4-4937-b880-52f9a49d9bfa · outbound

This paper cites Joint Discriminative and Generative Learning for Person Re-identification.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Joint Discriminative and Generative Learning for Person Re-identification

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:43:09.775500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7b496f6e-da04-4812-97ca-db92a850519c · outbound

This paper cites Trafficlens: A novel system for video-to-text conver- sion in multi-camera traffic environments,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Trafficlens: A novel system for video-to-text conver- sion in multi-camera traffic environments,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:13.417130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7d91cfaf-5882-4ede-88c1-5be018cfc9ee · outbound

This paper cites Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:43:09.652431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3377578f-c309-4f20-8dff-a41c5b3d58d6 · outbound

This paper cites Dad: A dashcam accident dataset,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Dad: A dashcam accident dataset,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:13.156123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7023c4c6-fb00-47d1-bced-8fdcceaeaa7c · outbound

This paper cites Cadp: A novel dataset for car accident detection and prediction from police reports,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Cadp: A novel dataset for car accident detection and prediction from police reports,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:12.935784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ac938ad0-26ac-48b0-bb4c-abbdf1f6b792 · outbound

This paper cites BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:05.206991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37a20d0b-c064-4295-a336-bf917aa640d9 · outbound

This paper cites Bulk-Boundary Correspondence in the Quantum Hall Effect.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Bulk-Boundary Correspondence in the Quantum Hall Effect

Reference 64

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T20:43:09.499953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c8fcad50-7dac-412b-834f-4a7807d1b478 · outbound

This paper cites ScVLM: Enhancing Vision-Language Model for Safety-Critical Event Understanding.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges ScVLM: Enhancing Vision-Language Model for Safety-Critical Event Understanding

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:43:09.342500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0a3fc6ec-0e31-46ea-8ae6-ceae26073d34 · outbound

This paper cites TrafficVLM: A Controllable Visual Language Model for Traffic Video Captioning.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges TrafficVLM: A Controllable Visual Language Model for Traffic Video Captioning

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:43:09.183265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bd857ff8-0747-497f-9992-a83301ee16c4 · outbound

This paper cites Crash: A context-aware attention-based framework for crash anticipation,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Crash: A context-aware attention-based framework for crash anticipation,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:12.649915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation db865e62-ee75-4544-9a9b-fff300e955c1 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:05.505355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6bb9525-c2e1-4c40-85f6-0f802bb7578c · outbound

This paper cites Slowfast networks for video recognition,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Slowfast networks for video recognition,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:12.373008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2bceff8e-4009-4032-a3e3-a4939c79406e · outbound

This paper cites Self-supervised anomaly detection: A survey and outlook,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Self-supervised anomaly detection: A survey and outlook,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:12.073096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eccf9c1e-c30b-4a11-8f5b-eb6dad9d780b · outbound

This paper cites Few-shot fast-adaptive anomaly detection,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Few-shot fast-adaptive anomaly detection,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:11.794299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation accd368e-1106-4b7f-afb7-66e42b0905cd · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:05.739969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7175c93d-b69a-401d-bf3f-7b819aa45d1c · outbound

This paper cites A Survey on Deep Learning Techniques for Video Anomaly Detection.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges A Survey on Deep Learning Techniques for Video Anomaly Detection

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:43:08.974237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e8ae7848-09dd-479d-af08-841db39a7e10 · outbound

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

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:05.873151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4f5159c-5cb2-409d-821f-611f7c353325 · outbound

This paper cites Convolutional lstm network: A machine learning approach for precipitation nowcasting,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Convolutional lstm network: A machine learning approach for precipitation nowcasting,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:11.532931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4d7b8a6e-051a-4240-a8bb-97af8b39e9e3 · outbound

This paper cites Video Anomaly Detection in 10 Years: A Survey and Outlook.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Video Anomaly Detection in 10 Years: A Survey and Outlook

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:06.026238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c97d037-7e18-4489-90a6-291bf823bb0d · outbound

This paper cites ViViT: A Video Vision Transformer.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges ViViT: A Video Vision Transformer

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:06.121709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f72261b-2234-4b57-95af-49b625d86f30 · outbound

This paper cites Bleu: a method for automatic evaluation of machine translation,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Bleu: a method for automatic evaluation of machine translation,

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:06.207625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7d6492b-55f1-461d-8dc3-ba0780cc329b · outbound

This paper cites Meteor: An automatic metric for mt evaluation with improved correlation with human judgments,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Meteor: An automatic metric for mt evaluation with improved correlation with human judgments,

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:06.264040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a39a169-d8a1-46ae-b00c-51b6e5263cc6 · outbound

This paper cites Cider: Consensus-based image description evaluation,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Cider: Consensus-based image description evaluation,

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:06.334184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1740d64-d080-4406-84b4-848f689fc2d8 · outbound

This paper cites Sutd-trafficqa: A question answering benchmark and an efficient network for autonomous driving,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Sutd-trafficqa: A question answering benchmark and an efficient network for autonomous driving,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:11.302437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f1022a6c-eaa7-465b-98f8-9d7c89144724 · outbound

This paper cites Causallp: Visual causal question answering with knowledge graphs,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Causallp: Visual causal question answering with knowledge graphs,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:11.171794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f5e412ca-fdd3-4dc9-9d33-1c1f18023792 · outbound

This paper cites CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes

Reference 83

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:43:08.600662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 046aa1f3-2440-482a-965b-f55308a449ac · outbound

This paper cites Bolstering causal reasoning in video question answering with multi-event causal discovery,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Bolstering causal reasoning in video question answering with multi-event causal discovery,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:11.013528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5e2b02e0-e7f2-42f7-ab20-4f32a1d7f23b · outbound

This paper cites Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:06.699602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa4a90d0-3923-4b4d-9ace-6a6bdbaffac6 · outbound

This paper cites Crash time matters: Hybridmamba for fine-grained temporal localization in traffic surveillance footage,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Crash time matters: Hybridmamba for fine-grained temporal localization in traffic surveillance footage,

Reference 86

Resolution: verified exact; raw_fallback, observed 2026-08-06T20:43:08.409542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation de6cdf92-6b1d-4796-9fd7-13e3091f4760 · outbound

This paper cites Deep Learning for Video Anomaly Detection: A Review.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Deep Learning for Video Anomaly Detection: A Review

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:06.875392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b092ca4-8f8d-4826-b6ab-28e387c2b98d · outbound

This paper cites Edge-Based Video Analytics: A Survey.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Edge-Based Video Analytics: A Survey

Reference 88

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:43:08.050272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 717e465f-ae8d-4ec1-b57d-2bf8175191c6 · outbound

This paper cites Synthetic datasets for autonomous driving: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. XX, NO. X, MONTH YEAR 24 A survey,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Synthetic datasets for autonomous driving: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. XX, NO. X, MONTH YEAR 24 A survey,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:10.906404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b0c01428-eb14-473d-abfa-5624e1ff7f9c · outbound

This paper cites Domain general- ization through meta-learning: a survey,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Domain general- ization through meta-learning: a survey,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:10.802463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d82ac71d-4534-4271-872a-011aa335da6f · outbound

This paper cites Generalized Out-of-Distribution Detection: A Survey.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Generalized Out-of-Distribution Detection: A Survey

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:07.278200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8303e124-e7be-4be8-b8b6-ea50d8515671 · outbound

This paper cites Carla: An open urban driving simulator,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Carla: An open urban driving simulator,

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:07.353734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 703f5a5a-6ad8-41be-8d29-cfa24306dade · outbound

This paper cites Perception, planning, control, and coordination for autonomous vehicles,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Perception, planning, control, and coordination for autonomous vehicles,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:10.701373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a8c7cc71-d1b7-48aa-930d-07d82ab268c5 · outbound

This paper cites A survey of the multi-sensor fusion object detection task in autonomous driving,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges A survey of the multi-sensor fusion object detection task in autonomous driving,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:43:10.573511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c44dce93-6c64-4a7e-b261-b9159e25900e · outbound

This paper cites A Survey on Efficient Vision-Language Models.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges A Survey on Efficient Vision-Language Models

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:07.662405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3e4edb3-e917-46a5-bf44-7a4e139f5ef5 · outbound

This paper cites Privacy-Preserving Video Anomaly Detection: A Survey.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Privacy-Preserving Video Anomaly Detection: A Survey

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:07.779736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ff580c3-6c20-4f3b-beec-639e3d156ff0 · outbound

This paper cites CRASH: Crash Recognition and Anticipation System Harnessing with Context-Aware and Temporal Focus Attentions.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges CRASH: Crash Recognition and Anticipation System Harnessing with Context-Aware and Temporal Focus Attentions

Reference 2024

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T20:43:10.363170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers