Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-21T04:52:12.045880Z
Paper Citation Record · LEDGER
As of 11 August 2026, this Paper Citation Record lists 80 of 80 outbound references and 0 inbound Pith citation observations for arXiv:2605.20838.
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 2026-05-21T04:52:12.045880Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
80 of 80 outbound references displayed
External citation measurements
No source-named external measurement is stored.
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Reference 1
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Source-reported events for the cited work
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Reference 10
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Reference 11
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Reference 12
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USV: Towards Understanding the User-generated Short-form Videos A Short Note about Kinetics-600
Reference 13
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USV: Towards Understanding the User-generated Short-form Videos Quo vadis, action recognition? a new model and the kinetics dataset
Reference 14
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USV: Towards Understanding the User-generated Short-form Videos Scaling egocentric vision: The epic-kitchens dataset
Reference 15
Source-reported events for the cited work
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USV: Towards Understanding the User-generated Short-form Videos The epic-kitchens dataset: Collection, challenges and base- lines.IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(11):4125–4141
Reference 16
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Reference 18
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Reference 19
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Reference 20
Source-reported events for the cited work
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Reference 21
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Reference 22
Source-reported events for the cited work
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Reference 23
Source-reported events for the cited work
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USV: Towards Understanding the User-generated Short-form Videos Self-supervised video representation learn- ing with odd-one-out networks
Reference 24
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USV: Towards Understanding the User-generated Short-form Videos The” something something” video database for learning and evaluating visual common sense
Reference 25
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USV: Towards Understanding the User-generated Short-form Videos Ava: A video dataset of spatio-temporally localized atomic visual actions
Reference 26
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Reference 27
Source-reported events for the cited work
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USV: Towards Understanding the User-generated Short-form Videos Deep residual learning for image recognition
Reference 28
Source-reported events for the cited work
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USV: Towards Understanding the User-generated Short-form Videos Activitynet: A large-scale video benchmark for human activity understanding.2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 961–970
Reference 29
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USV: Towards Understanding the User-generated Short-form Videos A hierarchical deep temporal model for group activity recognition
Reference 30
Source-reported events for the cited work
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USV: Towards Understanding the User-generated Short-form Videos Query- aware sparse coding for web multi-video summarization.In- formation Sciences, 478:152–166
Reference 31
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Reference 32
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Reference 33
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Reference 34
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USV: Towards Understanding the User-generated Short-form Videos The Kinetics Human Action Video Dataset
Reference 35
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Reference 37
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Reference 38
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Reference 39
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Reference 40
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Reference 41
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Reference 42
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Reference 43
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Reference 44
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USV: Towards Understanding the User-generated Short-form Videos PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding
Reference 45
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Reference 46
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Reference 47
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Reference 48
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Reference 49
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Reference 51
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Reference 52
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Reference 53
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Reference 54
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USV: Towards Understanding the User-generated Short-form Videos Enhancing micro-video understanding by harnessing external sounds
Reference 55
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Reference 56
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Reference 57
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Reference 58
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Reference 59
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Reference 60
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Reference 61
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Reference 62
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Reference 63
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Reference 64
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USV: Towards Understanding the User-generated Short-form Videos Two-stream con- volutional networks for action recognition in videos
Reference 65
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USV: Towards Understanding the User-generated Short-form Videos UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
Reference 66
Source-reported events for the cited work
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USV: Towards Understanding the User-generated Short-form Videos Learning Video Representations using Contrastive Bidirectional Transformer
Reference 67
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Reference 68
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USV: Towards Understanding the User-generated Short-form Videos A closer look at spatiotemporal convolutions for action recognition
Reference 69
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USV: Towards Understanding the User-generated Short-form Videos Self-supervised spatio-temporal representation learning for videos by predicting motion and appearance statistics
Reference 70
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USV: Towards Understanding the User-generated Short-form Videos Temporal segment net- works: Towards good practices for deep action recognition
Reference 71
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USV: Towards Understanding the User-generated Short-form Videos Neural multimodal co- operative learning toward micro-video understanding.IEEE Transactions on Image Processing, 29:1–14
Reference 72
Source-reported events for the cited work
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USV: Towards Understanding the User-generated Short-form Videos Audiovisual SlowFast Networks for Video Recognition
Reference 73
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USV: Towards Understanding the User-generated Short-form Videos Large-scale weakly supervised audio classifi- cation using gated convolutional neural network
Reference 75
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Observation 87c6bb0a-c7ad-4431-9ff8-b66b21ddad1a · outbound
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Reference 77
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Reference 78
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Observation 4b9d62c7-73e9-4a9d-894b-a74c7f7a74af · outbound
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Reference 79
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Observation b4eb4319-34b4-443e-b17e-00fe44866fbd · outbound
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Reference 80
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No inbound Pith citation observations are available.