Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:18:53.143452Z
Paper Citation Record · LEDGER
As of 16 August 2026, this Paper Citation Record lists 89 of 89 outbound references and 2 inbound Pith citation observations for arXiv:2505.13429.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:18:53.143452Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T14:32:22.239838Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-28T19:02:34.105509Z
89 of 89 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 89d10c2e-1c25-4f44-848f-5fe60e5a602c · outbound
Understanding Complexity in VideoQA via Visual Program Generation write newline
Reference 1
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Understanding Complexity in VideoQA via Visual Program Generation A shared neural substrate for action verbs and observed actions in human posterior parietal cortex
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Understanding Complexity in VideoQA via Visual Program Generation Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications
Reference 3
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Understanding Complexity in VideoQA via Visual Program Generation Neural module networks
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Understanding Complexity in VideoQA via Visual Program Generation Is space-time attention all you need for video understanding? In ICML, 2021
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Observation a0cf77f5-ca21-45f3-b652-b62bcca0071f · outbound
Understanding Complexity in VideoQA via Visual Program Generation Unresolved cited work
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Understanding Complexity in VideoQA via Visual Program Generation H., Lu, P., Nocedal, J., and Zhu, C
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Understanding Complexity in VideoQA via Visual Program Generation ActivityNet : A large-scale video benchmark for human activity understanding
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Understanding Complexity in VideoQA via Visual Program Generation Evaluating Large Language Models Trained on Code
Reference 9
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Understanding Complexity in VideoQA via Visual Program Generation E., et al
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Understanding Complexity in VideoQA via Visual Program Generation and Gr \`e zes, J
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Understanding Complexity in VideoQA via Visual Program Generation Brain activity during observation of actions
Reference 12
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Understanding Complexity in VideoQA via Visual Program Generation Unresolved cited work
Reference 13
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Understanding Complexity in VideoQA via Visual Program Generation K., Winn, J., and Zisserman, A
Reference 14
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Understanding Complexity in VideoQA via Visual Program Generation and Soto, A
Reference 15
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Understanding Complexity in VideoQA via Visual Program Generation Masked autoencoders as spatiotemporal learners
Reference 16
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Understanding Complexity in VideoQA via Visual Program Generation GPTScore: Evaluate as You Desire
Reference 17
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Understanding Complexity in VideoQA via Visual Program Generation VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling
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Understanding Complexity in VideoQA via Visual Program Generation Recursive visual programming
Reference 19
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Understanding Complexity in VideoQA via Visual Program Generation Adaptive computation time for recurrent neural networks
Reference 20
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Understanding Complexity in VideoQA via Visual Program Generation AgQA : A benchmark for compositional spatio-temporal reasoning
Reference 21
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Understanding Complexity in VideoQA via Visual Program Generation and Kembhavi, A
Reference 22
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Understanding Complexity in VideoQA via Visual Program Generation V., Sethi, R., and Ullman, J
Reference 23
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Observation bb2e49d6-2f87-4124-8aa8-6bdd98dfe28a · outbound
Understanding Complexity in VideoQA via Visual Program Generation Learning to reason: End-to-end module networks for visual question answering
Reference 24
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Observation db603616-1975-4174-a68f-6b4da6f4e151 · outbound
Understanding Complexity in VideoQA via Visual Program Generation Unresolved cited work
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Understanding Complexity in VideoQA via Visual Program Generation Unresolved cited work
Reference 26
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Understanding Complexity in VideoQA via Visual Program Generation Unresolved cited work
Reference 27
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Understanding Complexity in VideoQA via Visual Program Generation and Han, Y
Reference 28
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Understanding Complexity in VideoQA via Visual Program Generation CLEVR : A diagnostic dataset for compositional language and elementary visual reasoning
Reference 29
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Observation a4a7cd14-7886-4e6f-bf97-dabeafb351df · outbound
Understanding Complexity in VideoQA via Visual Program Generation Inferring and executing programs for visual reasoning
Reference 30
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Observation b0c07739-aa8c-4d9d-8cd4-07464fbab446 · outbound
Understanding Complexity in VideoQA via Visual Program Generation Unresolved cited work
Reference 31
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Observation cf7058e5-aedf-4cb1-9b87-09d8d8c7e458 · outbound
Understanding Complexity in VideoQA via Visual Program Generation W., Tapaswi, M., and Fidler, S
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Observation c3beb552-4555-4324-a1cd-0a9299354cdb · outbound
Understanding Complexity in VideoQA via Visual Program Generation and Bojar, O
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Understanding Complexity in VideoQA via Visual Program Generation Unresolved cited work
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Understanding Complexity in VideoQA via Visual Program Generation and Kramer, O
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Understanding Complexity in VideoQA via Visual Program Generation Dense-captioning events in videos
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Observation b345012c-50fb-41b0-bbc4-56890447f830 · outbound
Understanding Complexity in VideoQA via Visual Program Generation BLIP-2 : Bootstrapping language-image pre-training with frozen image encoders and large language models
Reference 37
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Understanding Complexity in VideoQA via Visual Program Generation MVBench : A comprehensive multi-modal video understanding benchmark
Reference 38
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Observation 08bbdb7b-4e67-43fd-9767-878955cf28ac · outbound
Understanding Complexity in VideoQA via Visual Program Generation A technique for the measurement of attitudes
Reference 39
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Observation d852b290-6d99-4c5b-aa41-734f0a6094bc · outbound
Understanding Complexity in VideoQA via Visual Program Generation Unresolved cited work
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Understanding Complexity in VideoQA via Visual Program Generation Unresolved cited work
Reference 41
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Observation 0ccd3f8a-0eff-47ba-b4fb-0de746ebedaf · outbound
Understanding Complexity in VideoQA via Visual Program Generation Unresolved cited work
Reference 42
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Understanding Complexity in VideoQA via Visual Program Generation L., Nejadasl, F
Reference 43
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Understanding Complexity in VideoQA via Visual Program Generation C., Adeli, E., and Li, F.-F
Reference 44
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Understanding Complexity in VideoQA via Visual Program Generation Y., Wu, J., Niebles, J
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Understanding Complexity in VideoQA via Visual Program Generation VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding
Reference 46
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Observation 04dd940f-c62f-4b38-98a0-001d23921706 · outbound
Understanding Complexity in VideoQA via Visual Program Generation Self-refine: Iterative refinement with self-feedback
Reference 47
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Observation 3e63888b-157e-4a7b-8945-445c70b22ef1 · outbound
Understanding Complexity in VideoQA via Visual Program Generation EgoSchema : A diagnostic benchmark for very long-form video language understanding
Reference 48
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Observation 157573ae-15cc-4564-a454-05c2a6117481 · outbound
Understanding Complexity in VideoQA via Visual Program Generation Unresolved cited work
Reference 49
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Understanding Complexity in VideoQA via Visual Program Generation and Vondrick, C
Reference 50
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Understanding Complexity in VideoQA via Visual Program Generation Unresolved cited work
Reference 51
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Understanding Complexity in VideoQA via Visual Program Generation GPT-4 technical report, 2023 b
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Understanding Complexity in VideoQA via Visual Program Generation and Schitter, C
Reference 53
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Understanding Complexity in VideoQA via Visual Program Generation A., Stretcu, O., Neubig, G., Pocz \'o s, B., and Mitchell, T
Reference 54
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Understanding Complexity in VideoQA via Visual Program Generation W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al
Reference 55
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Understanding Complexity in VideoQA via Visual Program Generation D., Ermon, S., and Finn, C
Reference 56
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Understanding Complexity in VideoQA via Visual Program Generation Annotating objects and relations in user-generated videos
Reference 57
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Understanding Complexity in VideoQA via Visual Program Generation HuggingGPT : Solving AI tasks with ChatGPT and its friends in hugging face
Reference 58
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Understanding Complexity in VideoQA via Visual Program Generation A., Varol, G., Wang, X., Farhadi, A., Laptev, I., and Gupta, A
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Understanding Complexity in VideoQA via Visual Program Generation Unresolved cited work
Reference 60
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Understanding Complexity in VideoQA via Visual Program Generation T., and Leordeanu, M
Reference 61
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Understanding Complexity in VideoQA via Visual Program Generation less is more
Reference 62
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Understanding Complexity in VideoQA via Visual Program Generation Modular visual question answering via code generation
Reference 63
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Understanding Complexity in VideoQA via Visual Program Generation ViperGPT : Visual inference via python execution for reasoning
Reference 64
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Understanding Complexity in VideoQA via Visual Program Generation T., Fu, J., Phan, M
Reference 65
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Understanding Complexity in VideoQA via Visual Program Generation A., Friedland, G., Elizalde, B., Ni, K., Poland, D., Borth, D., and Li, L.-J
Reference 66
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Understanding Complexity in VideoQA via Visual Program Generation Learning the curriculum with bayesian optimization for task-specific word representation learning
Reference 67
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Understanding Complexity in VideoQA via Visual Program Generation Learning the curriculum with bayesian optimization for task-specific word representation learning
Reference 68
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Understanding Complexity in VideoQA via Visual Program Generation P., and Ferrari, V
Reference 69
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Understanding Complexity in VideoQA via Visual Program Generation Neural discrete representation learning
Reference 70
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Understanding Complexity in VideoQA via Visual Program Generation Tarsier: Recipes for Training and Evaluating Large Video Description Models
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Understanding Complexity in VideoQA via Visual Program Generation InternVideo: General Video Foundation Models via Generative and Discriminative Learning
Reference 72
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Understanding Complexity in VideoQA via Visual Program Generation Language models with image descriptors are strong few-shot video-language learners
Reference 73
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Understanding Complexity in VideoQA via Visual Program Generation STC : A simple to complex framework for weakly-supervised semantic segmentation
Reference 74
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Understanding Complexity in VideoQA via Visual Program Generation B., and Gan, C
Reference 75
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Understanding Complexity in VideoQA via Visual Program Generation Unresolved cited work
Reference 76
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Understanding Complexity in VideoQA via Visual Program Generation Next-QA : Next phase of question-answering to explaining temporal actions
Reference 77
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Understanding Complexity in VideoQA via Visual Program Generation mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
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Understanding Complexity in VideoQA via Visual Program Generation W., Salakhutdinov, R., and Manning, C
Reference 79
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Understanding Complexity in VideoQA via Visual Program Generation Neural-symbolic VQA : Disentangling reasoning from vision and language understanding
Reference 80
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Understanding Complexity in VideoQA via Visual Program Generation Self-chained image-language model for video localization and question answering
Reference 81
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Reference 88
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