Paper Citation Record · LEDGER

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks

As of 19 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2508.19647.

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

pith.paper-citation-record.v1
2508.19647 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:40:12.915138Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:40:12.752377Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T15:40:13.179549Z

Reference resolution

29 of 29 outbound references displayed

verified exact3
verified fuzzy22
unresolved3
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation de367a5c-d7fb-4bf7-b3c2-d2b9bd34c66b · outbound

This paper cites UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T15:40:13.185906Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ff0fd62b-c670-4fe7-9f19-b3fedf2171f1 · outbound

This paper cites an unresolved cited work.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:40:13.646492Z

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Observation ae4efc8e-417f-4de2-be6b-7699e836b0c8 · outbound

This paper cites The data set consists of various dive ac- tions performed at four different heights of the spring: 3m, 5m, 7.5m, 10 meters.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks The data set consists of various dive ac- tions performed at four different heights of the spring: 3m, 5m, 7.5m, 10 meters

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.621842Z

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Observation d497b996-abc6-4622-82d9-f55657c78385 · outbound

This paper cites The au- thors encoded the action pattern into curvatures on the global timescale.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks The au- thors encoded the action pattern into curvatures on the global timescale

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.602808Z

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Observation 86716e7b-3ac5-4e8c-b876-da648e0f4379 · outbound

This paper cites Problem Setup Let the input pose sequence be X ∈ RB×F ×J×C, where B, F , J, and C denote batch size, time steps, joints, and feature dimension, respectively.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Problem Setup Let the input pose sequence be X ∈ RB×F ×J×C, where B, F , J, and C denote batch size, time steps, joints, and feature dimension, respectively

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.581420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6b3fb6b5-9f4f-4f78-a247-5d60de38d14e · outbound

This paper cites During training, we use a rolling window size of W = 7 and Gaussian noise standard deviation of σ = 0.1 to generate noisy input sub-pose sequences.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks During training, we use a rolling window size of W = 7 and Gaussian noise standard deviation of σ = 0.1 to generate noisy input sub-pose sequences

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.557847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 71a89e12-95c0-4910-ab9c-c09f692e81a3 · outbound

This paper cites Our method eliminates the need for manual annotations by utilizing Action Dynamics Metric (ADM) to identify key action transition points.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Our method eliminates the need for manual annotations by utilizing Action Dynamics Metric (ADM) to identify key action transition points

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.540574Z

Source-reported events for the cited work

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Observation a6863ef4-a22c-4fe0-a955-5d3669533953 · outbound

This paper cites Beyond hard workout: A multimodal framework for personalised running training with immersive technolo- gies,.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Beyond hard workout: A multimodal framework for personalised running training with immersive technolo- gies,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.518894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9349f0c5-f30b-4f62-84ff-903f80265b67 · outbound

This paper cites Graph atten- tion based proposal 3d convnets for action detection,.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Graph atten- tion based proposal 3d convnets for action detection,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.500448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 25f3cab9-cc3d-491c-8345-c37c769324aa · outbound

This paper cites Attention based spatial-temporal graph convolutional networks for traffic flow forecast- ing,.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Attention based spatial-temporal graph convolutional networks for traffic flow forecast- ing,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.481828Z

Source-reported events for the cited work

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Observation 595d9788-680b-4578-ae95-f5aa50228451 · outbound

This paper cites Revisiting anchor mechanisms for temporal action localization,.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Revisiting anchor mechanisms for temporal action localization,

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 618c10b8-0943-4338-9220-2ae2ed79dad4 · outbound

This paper cites Bottom-up temporal action lo- calization with mutual regularization,.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Bottom-up temporal action lo- calization with mutual regularization,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.441219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 65583d85-934a-4159-900c-1ba20f54fdba · outbound

This paper cites Visual Self-paced Iterative Learning for Unsupervised Temporal Action Localization.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Visual Self-paced Iterative Learning for Unsupervised Temporal Action Localization

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:40:13.136519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f6e7a5c8-c436-4636-b344-0a5a1a873e5f · outbound

This paper cites Multi-shot temporal event local- ization: a benchmark,.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Multi-shot temporal event local- ization: a benchmark,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.408282Z

Source-reported events for the cited work

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Observation d2156c5c-4a40-4158-a44c-6101254e9890 · outbound

This paper cites A hybrid attention mechanism for weakly-supervised temporal action localization,.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks A hybrid attention mechanism for weakly-supervised temporal action localization,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.389832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1a19126d-dff6-4d4a-951b-4bcca2428957 · outbound

This paper cites an unresolved cited work.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Unresolved cited work

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aa7735f2-19a6-440e-b684-7f89ef475a76 · outbound

This paper cites Back- ground suppression network for weakly-supervised tem- poral action localization,.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Back- ground suppression network for weakly-supervised tem- poral action localization,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.370219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fe5e68e6-7b56-4bfd-880d-13617b683bc9 · outbound

This paper cites Weakly-supervised action localization by generative attention modeling,.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Weakly-supervised action localization by generative attention modeling,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.353554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 36c9597b-a3b4-4425-8caf-6ae6b4ddc32e · outbound

This paper cites Adversarial background- aware loss for weakly-supervised temporal activity lo- calization,.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Adversarial background- aware loss for weakly-supervised temporal activity lo- calization,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.338808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6a771628-d57a-4259-ae2e-99f98718aefc · outbound

This paper cites Auto- matic moving pose grading for golf swing in sports,.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Auto- matic moving pose grading for golf swing in sports,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.321531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3de7becf-3d5c-4f07-9244-d550bc7e671e · outbound

This paper cites BID: Boundary-Interior Decoding for Unsupervised Temporal Action Localization Pre-Trainin.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks BID: Boundary-Interior Decoding for Unsupervised Temporal Action Localization Pre-Trainin

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:40:13.162111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e2987e27-f536-4a8c-b9cc-08afc29dd00d · outbound

This paper cites Survey of action recognition, spot- ting and spatio-temporal localization in soccer–current trends and research perspectives,.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Survey of action recognition, spot- ting and spatio-temporal localization in soccer–current trends and research perspectives,

Reference 22

Resolution: verified exact; raw_fallback, observed 2026-08-05T15:40:13.107671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0c33e6a4-447b-45d6-a3e3-8264287c3096 · outbound

This paper cites Finediving: A fine-grained dataset for procedure-aware action quality assessment,.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Finediving: A fine-grained dataset for procedure-aware action quality assessment,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.301575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e1c15175-b4bf-474f-9c70-aeef80dcbbeb · outbound

This paper cites Divenet: Dive action localization and physical pose parameter extraction for high performance training,.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Divenet: Dive action localization and physical pose parameter extraction for high performance training,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.283317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation be19bcc3-230e-4715-8618-f7f9b65d575c · outbound

This paper cites Curvature: A sig- nature for action recognition in video sequences,.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Curvature: A sig- nature for action recognition in video sequences,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.264126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 627e4c2e-bd64-41cb-9eca-a90946c3bc8a · outbound

This paper cites Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T15:40:12.897486Z

Source-reported events for the cited work

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Observation eeb724d4-d59e-4e9e-8821-8132cc4e5e6d · outbound

This paper cites Two-stream adaptive graph convolutional networks for skeleton-based action recognition,.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Two-stream adaptive graph convolutional networks for skeleton-based action recognition,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.244071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9cd5abb1-848d-4a1a-a451-22d8dc8204b3 · outbound

This paper cites Adaptive graph convolutional neural net- works,.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Adaptive graph convolutional neural net- works,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.223541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7fcba07a-f0fa-460a-b6f2-66290e7170bf · outbound

This paper cites Alphapose: Whole-body regional multi-person pose estimation and tracking in real-time,.

UTAL-GNN: Unsupervised Temporal Action Localization using Graph Neural Networks Alphapose: Whole-body regional multi-person pose estimation and tracking in real-time,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:40:13.205049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation de367a5c-d7fb-4bf7-b3c2-d2b9bd34c66b · inbound