Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:03:27.673880Z
Paper Citation Record · LEDGER
Simplifying Traffic Anomaly Detection with Video Foundation Models
As of 7 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2507.09338.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T00:22:12.609121Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T00:22:12.693223Z
Reference resolution
57 of 57 outbound references displayed
- verified exact1
- verified fuzzy43
- unresolved13
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation bccfd1b4-d00a-4dd3-90df-63d7d7e8f575 · outbound
This paper cites Vivit: A video vision transformer.
Simplifying Traffic Anomaly Detection with Video Foundation Models Vivit: A video vision transformer
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:03:23.695583Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 661a675b-711e-4094-965c-e4b8b5fdbb04 · outbound
This paper cites Layer Normalization.
Simplifying Traffic Anomaly Detection with Video Foundation Models Layer Normalization
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:03:23.758453Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 429e1493-0895-424a-b005-bdf7916b3a91 · outbound
This paper cites A Short Note on the Kinetics-700 Human Action Dataset.
Simplifying Traffic Anomaly Detection with Video Foundation Models A Short Note on the Kinetics-700 Human Action Dataset
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:03:23.826777Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2e7deb86-c165-42f8-88af-805bb0751be9 · outbound
This paper cites A simple framework for contrastive learning of visual representations.
Simplifying Traffic Anomaly Detection with Video Foundation Models A simple framework for contrastive learning of visual representations
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:03:23.922395Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7457ec50-1f98-4c7e-875e-b0c52dc843a4 · outbound
This paper cites The matthews cor- relation coefficient (mcc) should replace the roc auc as the standard metric for assessing binary classification.
Simplifying Traffic Anomaly Detection with Video Foundation Models The matthews cor- relation coefficient (mcc) should replace the roc auc as the standard metric for assessing binary classification
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:38.115329Z
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 bd222745-e546-4930-b44b-a7d767ebe9f8 · outbound
This paper cites Scaling egocentric vision: The epic-kitchens dataset.
Simplifying Traffic Anomaly Detection with Video Foundation Models Scaling egocentric vision: The epic-kitchens dataset
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:37.797049Z
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 1900116b-642a-4200-a0eb-60167936f26f · outbound
This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness.
Simplifying Traffic Anomaly Detection with Video Foundation Models Flashattention: Fast and memory-efficient exact attention with io-awareness
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:37.467482Z
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 fc03bc65-cbbd-41d6-92ec-f98cbf47b301 · outbound
This paper cites Cyclecrash: A dataset of bicycle collision videos for col- lision prediction and analysis.
Simplifying Traffic Anomaly Detection with Video Foundation Models Cyclecrash: A dataset of bicycle collision videos for col- lision prediction and analysis
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:37.178219Z
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 5589556e-3e15-4d1e-86f4-83b5fdcf6c1b · outbound
This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.
Simplifying Traffic Anomaly Detection with Video Foundation Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:03:24.302249Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c37ed47e-03c7-40f6-9694-56dc27d26a76 · outbound
This paper cites Dada: Driver attention prediction in driving accident scenarios.
Simplifying Traffic Anomaly Detection with Video Foundation Models Dada: Driver attention prediction in driving accident scenarios
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:36.947564Z
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 979825bd-5fa9-4a0c-a720-303606517295 · outbound
This paper cites Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark.
Simplifying Traffic Anomaly Detection with Video Foundation Models Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:03:24.414615Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 79969226-8611-40b0-b3df-c3865b68edb4 · outbound
This paper cites The” something something” video database for learning and evaluating visual common sense.
Simplifying Traffic Anomaly Detection with Video Foundation Models The” something something” video database for learning and evaluating visual common sense
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:36.652719Z
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 1954d4c9-d9f8-4c02-a62c-f5c401497bfa · outbound
This paper cites Don’t stop pretraining: Adapt language models to domains and tasks.
Simplifying Traffic Anomaly Detection with Video Foundation Models Don’t stop pretraining: Adapt language models to domains and tasks
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:36.371128Z
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 c92d2201-95ff-4109-9d81-7f268d50507c · outbound
This paper cites Masked autoencoders are scalable vision learners.
Simplifying Traffic Anomaly Detection with Video Foundation Models Masked autoencoders are scalable vision learners
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:03:24.614295Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b04be85b-6610-4e35-9a7b-66c5bc967dcb · outbound
This paper cites Mgmae: Motion guided masking for video masked autoencoding.
Simplifying Traffic Anomaly Detection with Video Foundation Models Mgmae: Motion guided masking for video masked autoencoding
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:36.087967Z
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 a7b84db8-76bb-4ca8-a398-0beb43d014f9 · outbound
This paper cites An enhanced traffic in- cident detection using factor analysis and weighted random forest algorithm.
Simplifying Traffic Anomaly Detection with Video Foundation Models An enhanced traffic in- cident detection using factor analysis and weighted random forest algorithm
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:35.683399Z
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 ec1bea89-c2af-4097-b900-1cb5571dfee6 · outbound
This paper cites The Kinetics Human Action Video Dataset.
Simplifying Traffic Anomaly Detection with Video Foundation Models The Kinetics Human Action Video Dataset
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:03:24.884011Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6f28dcb5-205d-4f6b-b30f-faacef738c0c · outbound
This paper cites First Place Solution to the ECCV 2024 BRAVO Challenge: Evaluating Robustness of Vision Foundation Models for Semantic Segmentation.
Simplifying Traffic Anomaly Detection with Video Foundation Models First Place Solution to the ECCV 2024 BRAVO Challenge: Evaluating Robustness of Vision Foundation Models for Semantic Segmentation
Reference 18
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T18:03:28.120920Z
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 6418d0b6-494c-4469-b9a3-fb6421601651 · outbound
This paper cites Your vit is secretly an image segmentation model.
Simplifying Traffic Anomaly Detection with Video Foundation Models Your vit is secretly an image segmentation model
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:35.369293Z
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 b743e67b-1496-48ac-9007-c3c931033331 · outbound
This paper cites Crash to not crash: Learn to identify dangerous vehicles using a simulator.
Simplifying Traffic Anomaly Detection with Video Foundation Models Crash to not crash: Learn to identify dangerous vehicles using a simulator
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:35.099161Z
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 d4425ac3-992c-4924-82b7-eb014653e47f · outbound
This paper cites Hmdb: a large video database for human motion recognition.
Simplifying Traffic Anomaly Detection with Video Foundation Models Hmdb: a large video database for human motion recognition
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:34.926894Z
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 7ecba662-150e-4a07-9c25-d01856f2f78b · outbound
This paper cites Unmasked teacher: Towards training-efficient video foundation models.
Simplifying Traffic Anomaly Detection with Video Foundation Models Unmasked teacher: Towards training-efficient video foundation models
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:34.768314Z
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 ec521009-6def-4ea2-ad91-266e97a6116a · outbound
This paper cites Videomamba: State space model for efficient video understanding.
Simplifying Traffic Anomaly Detection with Video Foundation Models Videomamba: State space model for efficient video understanding
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:34.525129Z
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 e4914e50-a79b-4dcb-aa4f-703b11535c58 · outbound
This paper cites Text-driven traffic anomaly detection with temporal high- frequency modeling in driving videos.
Simplifying Traffic Anomaly Detection with Video Foundation Models Text-driven traffic anomaly detection with temporal high- frequency modeling in driving videos
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:34.328413Z
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 00536d9e-1204-4106-9edb-beee98bad5d5 · outbound
This paper cites An interaction-scene collaborative representation framework for detecting traffic anomalies in driving videos.
Simplifying Traffic Anomaly Detection with Video Foundation Models An interaction-scene collaborative representation framework for detecting traffic anomalies in driving videos
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:34.150040Z
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 9ade1e04-7b83-4dbf-b256-ad66e368e7d4 · outbound
This paper cites Fu- ture frame prediction for anomaly detection–a new baseline.
Simplifying Traffic Anomaly Detection with Video Foundation Models Fu- ture frame prediction for anomaly detection–a new baseline
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:33.850887Z
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 60a3bbe4-f3e2-4ec9-a2ab-d55d56b174c9 · outbound
This paper cites A convnet for the 2020s.
Simplifying Traffic Anomaly Detection with Video Foundation Models A convnet for the 2020s
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:03:25.653452Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 436a92dd-7cb3-4b7a-a14e-ad67dd1b4bdc · outbound
This paper cites Video swin transformer.
Simplifying Traffic Anomaly Detection with Video Foundation Models Video swin transformer
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:33.554293Z
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 62af7b80-8dd7-4ac7-95df-ad4716df2534 · outbound
This paper cites Decoupled Weight Decay Regularization.
Simplifying Traffic Anomaly Detection with Video Foundation Models Decoupled Weight Decay Regularization
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:03:25.796075Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation af920b0b-37ce-4017-a54a-269f41db3066 · outbound
This paper cites Remembering history with convolutional lstm for anomaly detection.
Simplifying Traffic Anomaly Detection with Video Foundation Models Remembering history with convolutional lstm for anomaly detection
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:33.352563Z
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 9b024d91-3053-4c89-8de9-7ab60b8df0fc · outbound
This paper cites Foundation models for video understanding: A survey.
Simplifying Traffic Anomaly Detection with Video Foundation Models Foundation models for video understanding: A survey
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:33.128844Z
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 b2b82a26-77c2-41be-ae67-b5f30d874b66 · outbound
This paper cites Howto100m: Learning a text-video embedding by watching hundred million narrated video clips.
Simplifying Traffic Anomaly Detection with Video Foundation Models Howto100m: Learning a text-video embedding by watching hundred million narrated video clips
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:32.901464Z
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 61aa7cbe-36d8-423d-9765-0fcfdaf6c484 · outbound
This paper cites End-to-end learning of visual representations from uncurated instruc- tional videos.
Simplifying Traffic Anomaly Detection with Video Foundation Models End-to-end learning of visual representations from uncurated instruc- tional videos
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:32.593427Z
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 d9ea70f7-53a9-4e44-bd17-56adc581b3d1 · outbound
This paper cites an unresolved cited work.
Simplifying Traffic Anomaly Detection with Video Foundation Models Unresolved cited work
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:03:26.140435Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 28b5ecdd-4a3e-4a73-8052-f96a13734986 · outbound
This paper cites Prompttad: Object-prompt enhanced traffic anomaly detection.
Simplifying Traffic Anomaly Detection with Video Foundation Models Prompttad: Object-prompt enhanced traffic anomaly detection
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:32.354480Z
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 2362e116-ac71-4d0a-a1e5-7ca7f5973c57 · outbound
This paper cites Learning transferable visual models from natural language supervi- sion.
Simplifying Traffic Anomaly Detection with Video Foundation Models Learning transferable visual models from natural language supervi- sion
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:32.188436Z
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 f9f1ccad-ea79-46ff-b6b9-e964a9f62be4 · outbound
This paper cites Memory-augmented online video anomaly detection.
Simplifying Traffic Anomaly Detection with Video Foundation Models Memory-augmented online video anomaly detection
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:31.964505Z
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 796d5331-2bcd-42ed-80fe-0baa65eefca0 · outbound
This paper cites Sigma: Sinkhorn-guided masked video mod- eling.
Simplifying Traffic Anomaly Detection with Video Foundation Models Sigma: Sinkhorn-guided masked video mod- eling
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:31.816901Z
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 17a4f3b1-f50e-493a-9203-a7ed4b305f88 · outbound
This paper cites Learning to predict collision risk from sim- ulated video data.
Simplifying Traffic Anomaly Detection with Video Foundation Models Learning to predict collision risk from sim- ulated video data
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:31.679589Z
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 8d19e9f5-c8cf-4d63-8148-c807d7629a6d · outbound
This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.
Simplifying Traffic Anomaly Detection with Video Foundation Models UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:03:26.500006Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ee92a948-9e5c-4cfa-920f-10b71e4a43ba · outbound
This paper cites Masked motion encoding for self-supervised video representation learning.
Simplifying Traffic Anomaly Detection with Video Foundation Models Masked motion encoding for self-supervised video representation learning
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:31.473731Z
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 29625acc-aa02-45db-a3dd-c55f914eee67 · outbound
This paper cites Deep learning applied to road accident detection with transfer learning and synthetic im- ages.
Simplifying Traffic Anomaly Detection with Video Foundation Models Deep learning applied to road accident detection with transfer learning and synthetic im- ages
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:31.306431Z
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 45c85351-4f3b-489e-a18c-3b6906e1de56 · outbound
This paper cites Smile: Infusing spatial and motion se- mantics in masked video learning.
Simplifying Traffic Anomaly Detection with Video Foundation Models Smile: Infusing spatial and motion se- mantics in masked video learning
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:31.106432Z
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 4839ee61-c23f-4c65-9d5d-e170f9f049b1 · outbound
This paper cites Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training.
Simplifying Traffic Anomaly Detection with Video Foundation Models Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:30.940404Z
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 5d070693-5070-4602-8a43-dd64daa12313 · outbound
This paper cites A closer look at spatiotemporal convolutions for action recognition.
Simplifying Traffic Anomaly Detection with Video Foundation Models A closer look at spatiotemporal convolutions for action recognition
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:30.784533Z
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 36dce677-0167-447d-b858-471cb5bf498c · outbound
This paper cites Attention is all you need.
Simplifying Traffic Anomaly Detection with Video Foundation Models Attention is all you need
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:03:26.879535Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d7b801be-0b65-45cb-9974-de2d9e7fcec3 · outbound
This paper cites The BRA VO Semantic Segmentation Challenge Results in UNCV2024.
Simplifying Traffic Anomaly Detection with Video Foundation Models The BRA VO Semantic Segmentation Challenge Results in UNCV2024
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:30.634075Z
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 376e21f5-01ce-4aad-9338-bbaf159503d5 · outbound
This paper cites Rs2g: Data-driven scene-graph extraction and embedding for ro- bust autonomous perception and scenario understanding.
Simplifying Traffic Anomaly Detection with Video Foundation Models Rs2g: Data-driven scene-graph extraction and embedding for ro- bust autonomous perception and scenario understanding
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:30.469803Z
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 28b3be1b-0704-46d6-9758-bd0e09ca936d · outbound
This paper cites Abnormal event detection in videos using hy- brid spatio-temporal autoencoder.
Simplifying Traffic Anomaly Detection with Video Foundation Models Abnormal event detection in videos using hy- brid spatio-temporal autoencoder
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:30.294941Z
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 cdf9b9ce-852c-411a-8005-06e59552d406 · outbound
This paper cites Videomae v2: Scaling video masked autoencoders with dual masking.
Simplifying Traffic Anomaly Detection with Video Foundation Models Videomae v2: Scaling video masked autoencoders with dual masking
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:30.076946Z
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 d4cfcded-5b7d-4c01-b59c-48d7ba0fe133 · outbound
This paper cites Masked video distillation: Rethinking masked feature mod- eling for self-supervised video representation learning.
Simplifying Traffic Anomaly Detection with Video Foundation Models Masked video distillation: Rethinking masked feature mod- eling for self-supervised video representation learning
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:29.905668Z
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 d41e977d-7339-43fa-8678-b9771593a345 · outbound
This paper cites Internvideo2: Scaling foundation models for mul- timodal video understanding.
Simplifying Traffic Anomaly Detection with Video Foundation Models Internvideo2: Scaling foundation models for mul- timodal video understanding
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:29.741186Z
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 e9e9ce6b-3316-4268-8d96-313129ca2f80 · outbound
This paper cites Recurring the transformer for video action recognition.
Simplifying Traffic Anomaly Detection with Video Foundation Models Recurring the transformer for video action recognition
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:29.475904Z
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 4afd28b7-5f1f-4362-b869-179e7b41ef5f · outbound
This paper cites Dota: unsupervised detec- tion of traffic anomaly in driving videos.
Simplifying Traffic Anomaly Detection with Video Foundation Models Dota: unsupervised detec- tion of traffic anomaly in driving videos
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:29.306398Z
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 3be299d7-da65-43e5-ab2d-7d7d3048955b · outbound
This paper cites Bdd100k: A diverse driving dataset for heterogeneous multitask learning.
Simplifying Traffic Anomaly Detection with Video Foundation Models Bdd100k: A diverse driving dataset for heterogeneous multitask learning
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:29.105493Z
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 18749a73-6617-4a64-855e-69118ab4983c · outbound
This paper cites Scaling vision transformers.
Simplifying Traffic Anomaly Detection with Video Foundation Models Scaling vision transformers
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:28.866362Z
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 2f465c5b-39c0-4cc8-9faf-c9555870332f · outbound
This paper cites Spatio-temporal feature encoding for traffic accident detection in vanet environment.
Simplifying Traffic Anomaly Detection with Video Foundation Models Spatio-temporal feature encoding for traffic accident detection in vanet environment
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:03:28.601040Z
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.
Pith citing papers
Observation fd94202c-0eea-4329-adca-55590de9a3fe · inbound
Asleep at the Wheel: JEPA's Limitations in Evaluating Novel Driving Data cites this paper.
Asleep at the Wheel: JEPA's Limitations in Evaluating Novel Driving Data Simplifying Traffic Anomaly Detection with Video Foundation Models
Reference 10
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T00:22:12.697368Z
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.