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Paper Citation Record · LEDGER

HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1712.09374.

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

pith.paper-citation-record.v1
1712.09374 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:32:24.123571Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T09:45:39.591861Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6604f8a3-852e-449d-b1ea-1f97d7eb1b52 · inbound

Fire360: A Benchmark for Robust Perception and Episodic Memory in Degraded 360-Degree Firefighting Videos cites this paper.

Fire360: A Benchmark for Robust Perception and Episodic Memory in Degraded 360-Degree Firefighting Videos HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-07T11:32:24.123571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:32:24.123571Z digest=sha256:89d45d8ab4577026a250ab0473e76531278df4abfdfd467f9785d7080bf01813

Observation 6ae632a7-520d-43b8-96f3-33fb4130edf5 · inbound

VidEvent: A Large Dataset for Understanding Dynamic Evolution of Events in Videos cites this paper.

VidEvent: A Large Dataset for Understanding Dynamic Evolution of Events in Videos HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-07T11:26:13.878857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:26:13.878857Z digest=sha256:25c8b143b363db72e3f26ca09ea552e197a9ff5537ed02696273fa03fadbac6b

Observation 9efbab19-1769-48cb-9f6a-b522e115e48d · inbound

VideoForest: Person-Anchored Hierarchical Reasoning for Cross-Video Question Answering cites this paper.

VideoForest: Person-Anchored Hierarchical Reasoning for Cross-Video Question Answering HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-06T04:49:42.667270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:49:42.667270Z digest=sha256:e30d0f996a44e0befdd5dc30fe0b3fa1189c106bfe0f5f274e2afb08335168e5

Observation ca3f30a2-58d2-423b-967e-17ac87f14347 · inbound

HumanNet: Scaling Human-centric Video Learning to One Million Hours cites this paper.

HumanNet: Scaling Human-centric Video Learning to One Million Hours HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-05-11T05:10:54.103595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-11T00:51:08.414394Z digest=sha256:4cc5e9a709f794d372ec6c88a2faeac1b1aae13b1d3c56cd9f5445b8820239bf

Observation d6038600-1d64-4622-ba92-4ecdbb549086 · inbound

Learning to Deny: Action Denial in Multimodal Large Language Models cites this paper.

Learning to Deny: Action Denial in Multimodal Large Language Models HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization

Reference 86

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T09:45:39.593064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-07-01T06:21:09.996386Z digest=sha256:8847a34da9a24d0688bb935cd131845df72fbddc210b7fa3a0937f4e088e6fcd