Paper Citation Record · LEDGER

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks

As of 14 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2412.01132.

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

pith.paper-citation-record.v1
2412.01132 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:44:08.780881Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T18:38:16.204012Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T00:15:52.849428Z

Reference resolution

57 of 57 outbound references displayed

verified exact0
verified fuzzy31
unresolved26
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 129e97fc-118e-48b7-a6ed-537ce680ce53 · outbound

This paper cites Video question answering: Datasets, algorithms and challenges, 2022.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Video question answering: Datasets, algorithms and challenges, 2022

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T04:44:08.628106Z

Source-reported events for the cited work

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Observation dfad524f-48b6-47a8-843c-d10e471f9518 · outbound

This paper cites Videoqa in the era of llms: An empirical study, 2024.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Videoqa in the era of llms: An empirical study, 2024

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.158989Z

Source-reported events for the cited work

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Observation 44aedcde-13e9-4ce0-96ad-770869e13167 · outbound

This paper cites Francis, and Alessandro Oltramari.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Francis, and Alessandro Oltramari

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.152971Z

Source-reported events for the cited work

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Observation 7f603df1-b510-4b89-9090-6979884feb52 · outbound

This paper cites Carla: An open urban driving simulator, 2017.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Carla: An open urban driving simulator, 2017

Reference 4

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Source-reported events for the cited work

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Observation 4761f4e7-63f7-48f4-8915-06f870a3c798 · outbound

This paper cites Lingoqa: Visual question answering for autonomous driving, 2024.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Lingoqa: Visual question answering for autonomous driving, 2024

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.142418Z

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Observation 20c7a9e5-ca6e-4f0d-967c-a60cf68fbd61 · outbound

This paper cites Align and aggregate: Compositional reasoning with video alignment and answer aggregation for video question-answering, 2024.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Align and aggregate: Compositional reasoning with video alignment and answer aggregation for video question-answering, 2024

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.135003Z

Source-reported events for the cited work

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Observation c9995d22-7b77-4980-8a48-530f2f7d47e3 · outbound

This paper cites GPT-4o: System Card.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks GPT-4o: System Card

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.126898Z

Source-reported events for the cited work

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Observation 872b3005-0b4b-4fe1-a58a-d34a9b1d47f3 · outbound

This paper cites Dynamic spatio-temporal graph reasoning for videoqa with self-supervised event recognition.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Dynamic spatio-temporal graph reasoning for videoqa with self-supervised event recognition

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.119502Z

Source-reported events for the cited work

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Observation 5248eca9-14de-4926-9522-8ba932fb945a · outbound

This paper cites Recognizing an action using its name: A knowledge-based approach.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Recognizing an action using its name: A knowledge-based approach

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.112135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1f6253e7-a2ae-458e-8b2b-88901f668cc4 · outbound

This paper cites Video representation learning with deep neural networks.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Video representation learning with deep neural networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.104141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f79878ee-26e8-49f2-a65b-bff1bcd36640 · outbound

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

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Video question answering via gradually refined attention over appearance and motion

Reference 11

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Source-reported events for the cited work

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Observation 288832a7-3821-4f8b-a103-91085ed142b5 · outbound

This paper cites Vilbert: Pretraining task-agnostic visiolinguistic representa- tions for vision-and-language tasks.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Vilbert: Pretraining task-agnostic visiolinguistic representa- tions for vision-and-language tasks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.092676Z

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Observation b4658756-8c62-4aa0-967b-6cc2596ce35a · outbound

This paper cites Compositional attention networks with two-stream fusion for video question answering.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Compositional attention networks with two-stream fusion for video question answering

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.083934Z

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Observation eff6c892-b8f5-4b73-bede-f3b5f70725ca · outbound

This paper cites Visualbert: A simple and performant baseline for vision and language, 2019.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Visualbert: A simple and performant baseline for vision and language, 2019

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.075921Z

Source-reported events for the cited work

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Observation e93753ee-4fb1-42ea-af3b-7a21806dd383 · outbound

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

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Activitynet: A large-scale video benchmark for human activity understanding

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.068111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c73926d5-4343-4d0b-af80-0c22f0799a30 · outbound

This paper cites Tgif: A new dataset and benchmark on animated gif description, 2016.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Tgif: A new dataset and benchmark on animated gif description, 2016

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.061016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5d00888a-1c89-44a0-af8f-a916f2ee03a5 · outbound

This paper cites Next-qa:next phase of question-answering to explaining temporal actions, 2021.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Next-qa:next phase of question-answering to explaining temporal actions, 2021

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.052959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 79cac09c-c190-41f0-b3dd-f544ee9bf5d8 · outbound

This paper cites Gemini: A family of highly capable multimodal models, 2024.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Gemini: A family of highly capable multimodal models, 2024

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.045004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 08f1446b-302c-487e-971f-60fdc007da69 · outbound

This paper cites Video instruction tuning with synthetic data, 2024.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Video instruction tuning with synthetic data, 2024

Reference 19

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Source-reported events for the cited work

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Observation c726ee96-410e-46ec-9cbb-1a3f447843dd · outbound

This paper cites Ego4d: Around the world in 3,000 hours of egocentric video, 2022.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Ego4d: Around the world in 3,000 hours of egocentric video, 2022

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.035218Z

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Observation e9d99f4f-780b-4100-9884-961ae8c0217f · outbound

This paper cites Egoschema: A diagnostic benchmark for very long-form video language understanding.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Egoschema: A diagnostic benchmark for very long-form video language understanding

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.028613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7573b07a-f401-4f63-8698-9066dda52739 · outbound

This paper cites CinePile: A Long Video Question Answering Dataset and Benchmark.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 22

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Observation 8d4d09cf-3d89-4d80-96f0-5dc03a22fba5 · outbound

This paper cites Mist: Medical image segmenta- tion transformer with convolutional attention mixing (cam) decoder.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Mist: Medical image segmenta- tion transformer with convolutional attention mixing (cam) decoder

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.020688Z

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Observation 757f607f-27dc-47d1-b85b-0de084d1c596 · outbound

This paper cites A multi-world approach to question answering about real-world scenes based on uncertain input.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks A multi-world approach to question answering about real-world scenes based on uncertain input

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:09.011374Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5da1ab1b-8c51-41a9-b746-be81af226e7f · outbound

This paper cites an unresolved cited work.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Unresolved cited work

Reference 25

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Observation 3b0ec725-2eeb-40b2-bb12-70ed4eb13ed5 · outbound

This paper cites Sutd-trafficqa: A question answering benchmark and an efficient network for video reasoning over traffic events.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Sutd-trafficqa: A question answering benchmark and an efficient network for video reasoning over traffic events

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:08.994916Z

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Observation 5d18766e-e981-475a-bb57-8f40c2e22232 · outbound

This paper cites Argos vision: Advanced computer vision solutions, 2024.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Argos vision: Advanced computer vision solutions, 2024

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:08.988205Z

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Observation 702591f2-84be-46f0-8228-b9617433be06 · outbound

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

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Can i trust your answer? visually grounded video question answering

Reference 28

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Observation 61059adc-ffe0-4a6c-9a91-a63ae3a9cb7b · outbound

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

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks LLaVA-OneVision: Easy Visual Task Transfer

Reference 29

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

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

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This paper cites How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Reference 31

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Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Videollama 2: Advancing spatial-temporal modeling and audio understanding in video-llms, 2024

Reference 32

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Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model

Reference 33

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This paper cites Improved baselines with visual instruction tuning.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Improved baselines with visual instruction tuning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:08.969301Z

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This paper cites Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning

Reference 35

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Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

Reference 36

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This paper cites InternLM2 Technical Report.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks InternLM2 Technical Report

Reference 37

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Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

Reference 38

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This paper cites Minesh Mathew.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Minesh Mathew

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:08.961214Z

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Observation d2ac603b-7010-4854-ad84-de5d025caa2d · outbound

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Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Unresolved cited work

Reference 40

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Observation 2987ac11-a28d-4c06-8098-a0ae3a7b7454 · outbound

This paper cites Infographicvqa.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Infographicvqa

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:08.947975Z

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Observation 59ea8c61-fff6-4480-89f1-6123798168da · outbound

This paper cites Towards vqa models that can read.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Towards vqa models that can read

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:08.941345Z

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Observation 3a21812c-f164-45cd-97ac-fd6c8ce900a6 · outbound

This paper cites OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-12T04:44:08.742181Z

Source-reported events for the cited work

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Observation e0bb42d2-7d75-4433-9fe4-2636430cfaee · outbound

This paper cites Mmt-bench: A comprehensive multimodal benchmark for evaluating large vision-language models towards multitask agi, 2024.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Mmt-bench: A comprehensive multimodal benchmark for evaluating large vision-language models towards multitask agi, 2024

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-12T04:44:08.746244Z

Source-reported events for the cited work

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Observation 2f90c4b2-758f-4fed-ba1c-122d09534858 · outbound

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

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Learning transferable visual models from natural language supervision

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:08.932031Z

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Observation 824b2fb0-b4b9-43eb-ae50-573ec8937a95 · outbound

This paper cites Beats: Audio pre-training with acoustic tokenizers.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Beats: Audio pre-training with acoustic tokenizers

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:08.923785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 40d87ff2-f9ca-4180-a1f5-dfbe488898d5 · outbound

This paper cites an unresolved cited work.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Unresolved cited work

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-12T04:44:08.754826Z

Source-reported events for the cited work

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Observation 3f8fb43b-7709-4f52-b0a4-a0b2a3109792 · outbound

This paper cites Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T04:44:08.757039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd311754-fd02-46e3-b8ed-c8f7b2ddd62a · outbound

This paper cites LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-12T04:44:08.759431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0de5971-23ab-4a76-a6ac-dc29b659efeb · outbound

This paper cites Frozen in time: A joint video and image encoder for end-to-end retrieval.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Frozen in time: A joint video and image encoder for end-to-end retrieval

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:08.911658Z

Source-reported events for the cited work

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Observation bdeeb4e0-ac6c-41f9-a072-332ba319b5f3 · outbound

This paper cites InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-12T04:44:08.765337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45b27448-3032-4290-8177-014191844ef8 · outbound

This paper cites Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:08.903229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f6484ece-c312-4ca4-8b39-8a7d9917b913 · outbound

This paper cites A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T04:44:08.770534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac9974fc-3fc7-408f-802f-d469af7619dd · outbound

This paper cites Advancing high-resolution video-language representation with large-scale video transcriptions.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Advancing high-resolution video-language representation with large-scale video transcriptions

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T04:44:08.772811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 683195b3-4202-4a26-8627-2b7242cd4580 · outbound

This paper cites Gonzalez, Ion Stoica, and Eric P.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Gonzalez, Ion Stoica, and Eric P

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T04:44:08.775565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59587831-6f21-4d66-b0dd-7251b5939791 · outbound

This paper cites Llava-next: Improved reasoning, ocr, and world knowledge, January 2024.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks Llava-next: Improved reasoning, ocr, and world knowledge, January 2024

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-12T04:44:08.778585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce52ce43-89c2-4ffe-a81b-12acc45d3999 · outbound

This paper cites How many cars can you see?.

Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks How many cars can you see?

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:44:08.883904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation f316b1d1-7c9a-494e-96fa-35b68ef33342 · inbound

Bridging Time and Space: Decoupled Spatio-Temporal Alignment for Video Grounding cites this paper.

Bridging Time and Space: Decoupled Spatio-Temporal Alignment for Video Grounding Eyes on the Road: State-of-the-Art Video Question Answering Models Assessment for Traffic Monitoring Tasks

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:15:52.858304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.