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Paper Citation Record · LEDGER

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA

As of 8 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2511.05611.

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

pith.paper-citation-record.v1
2511.05611 v2

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T23:48:44.328448Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T07:44:24.069899Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T08:04:28.908338Z

Reference resolution

53 of 53 outbound references displayed

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Outbound references

Observation 9e0ea3c0-484c-4da4-8532-bb82cb35a248 · outbound

This paper cites Sportsmot: A large multi-object tracking dataset in multiple sports scenes,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Sportsmot: A large multi-object tracking dataset in multiple sports scenes,

Reference 1

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Observation 89b9994d-db43-43bf-8330-960669190d3d · outbound

This paper cites Multisports: A multi-person video dataset of spatio- temporally localized sports actions,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Multisports: A multi-person video dataset of spatio- temporally localized sports actions,

Reference 2

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Observation 2f2d0e4b-48fa-47b6-98e4-eb003227ee18 · outbound

This paper cites Finegym: A hierarchical video dataset for fine- grained action understanding,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Finegym: A hierarchical video dataset for fine- grained action understanding,

Reference 3

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Observation 17d80826-f434-4af2-9231-ca20ec773623 · outbound

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

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Finediving: A fine-grained dataset for procedure- aware action quality assessment,

Reference 4

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Observation 48659428-359a-4e07-a039-0aaa88388d30 · outbound

This paper cites Uncertainty-aware score distribution learning for action quality assessment,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Uncertainty-aware score distribution learning for action quality assessment,

Reference 5

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Observation 6be79727-51f2-49fc-a25f-d50af90cbf98 · outbound

This paper cites Group-aware contrastive regression for action quality assessment,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Group-aware contrastive regression for action quality assessment,

Reference 6

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Observation 2ba947a2-ed84-4820-9abf-8b9110a52cb8 · outbound

This paper cites Quo vadis, action recognition? a new model and the kinetics dataset,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Quo vadis, action recognition? a new model and the kinetics dataset,

Reference 7

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Observation fa80a1f2-875d-45a1-9f74-370ae2c10739 · outbound

This paper cites Online quality assessment of human movement from skeleton data,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Online quality assessment of human movement from skeleton data,

Reference 8

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Observation 5f65d357-26e4-4bc0-91ee-6d3a37c297e8 · outbound

This paper cites Visual and human-interpretable feedback for assisting physical activity,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Visual and human-interpretable feedback for assisting physical activity,

Reference 9

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Observation 14898589-6fd9-43c3-b546-8857d65c4a28 · outbound

This paper cites A video-based augmented reality system for human-in-the-loop muscle strength assessment of juvenile dermatomyosi- tis,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA A video-based augmented reality system for human-in-the-loop muscle strength assessment of juvenile dermatomyosi- tis,

Reference 10

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Observation 46910a52-a870-4273-95b0-8734940c03c5 · outbound

This paper cites Who’s better? who’s best? pairwise deep ranking for skill determination,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Who’s better? who’s best? pairwise deep ranking for skill determination,

Reference 11

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Observation 1280945e-0608-4392-b76f-b47d25f2a366 · outbound

This paper cites The pros and cons: Rank-aware temporal attention for skill determination in long videos,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA The pros and cons: Rank-aware temporal attention for skill determination in long videos,

Reference 12

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Observation 49559630-4e96-4c20-a2a9-9604fce7f13f · outbound

This paper cites Spatio-temporal adaptive network with bidirectional temporal difference for action recognition,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Spatio-temporal adaptive network with bidirectional temporal difference for action recognition,

Reference 13

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Observation c6770058-f6ab-4644-857b-2ce40b022547 · outbound

This paper cites Agpn: Action granularity pyramid network for video action recognition,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Agpn: Action granularity pyramid network for video action recognition,

Reference 14

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Observation cf6173b2-9ff8-4b39-b93c-06b6f76acfac · outbound

This paper cites Question-aware global-local video understand- ing network for audio-visual question answering,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Question-aware global-local video understand- ing network for audio-visual question answering,

Reference 15

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Observation 4f38f7cc-13f0-4bd4-8215-1915008ec481 · outbound

This paper cites Learning to score olympic events,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Learning to score olympic events,

Reference 16

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Observation c1f28496-e16d-4576-bc1d-1453b580651c · outbound

This paper cites Learning to score figure skating sport videos,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Learning to score figure skating sport videos,

Reference 17

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Observation 77fecffd-ab3d-4207-aceb-c6e4e17938b9 · outbound

This paper cites Attentive spatio-temporal representation learning for diving classification,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Attentive spatio-temporal representation learning for diving classification,

Reference 18

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Observation a2bae062-ed25-4644-9326-1a8fe809b25f · outbound

This paper cites Multi-Stage Contrastive Regression for Action Quality Assessment.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Multi-Stage Contrastive Regression for Action Quality Assessment

Reference 19

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Observation 30af414f-2b09-41f3-b95e-41e7b8d32460 · outbound

This paper cites Fineparser: A fine-grained spatio-temporal action parser for human-centric action quality assessment,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Fineparser: A fine-grained spatio-temporal action parser for human-centric action quality assessment,

Reference 20

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Observation ab61d71b-7ef0-4740-95d1-4eb3d03bb2ba · outbound

This paper cites Efficient spatio-temporal contrastive learning for skeleton-based 3d action recognition,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Efficient spatio-temporal contrastive learning for skeleton-based 3d action recognition,

Reference 21

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Observation 40755d3e-f246-425a-bec6-cbb35e7c4530 · outbound

This paper cites Glimpse and focus: Global and local-scale graph convolution network for skeleton-based action recognition,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Glimpse and focus: Global and local-scale graph convolution network for skeleton-based action recognition,

Reference 22

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Observation 4bcba255-a9e4-46f9-8c22-c34119ef3af9 · outbound

This paper cites Learning heterogeneous spatial-temporal context for skeleton-based action recognition,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Learning heterogeneous spatial-temporal context for skeleton-based action recognition,

Reference 23

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Observation 1b5c170c-7920-4b91-ab98-d5cdc9b37baf · outbound

This paper cites Assessing the quality of actions,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Assessing the quality of actions,

Reference 24

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Observation 4dec9edd-2145-4df9-bdff-7052809e9aec · outbound

This paper cites Support vector regression,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Support vector regression,

Reference 25

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Observation 679be7d3-0d0a-4594-bf0c-1562ed374526 · outbound

This paper cites What and how well you performed? a multitask learning approach to action quality assessment,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA What and how well you performed? a multitask learning approach to action quality assessment,

Reference 26

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Observation baa0fb18-4202-4b2f-a336-738e21b2d3ca · outbound

This paper cites Hierarchical graph convolutional networks for action quality assessment,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Hierarchical graph convolutional networks for action quality assessment,

Reference 27

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Observation 12dbc821-abd3-40ae-83bb-618b60e4516d · outbound

This paper cites Hybrid dynamic-static context-aware attention network for action assessment in long videos,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Hybrid dynamic-static context-aware attention network for action assessment in long videos,

Reference 28

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Observation c37a65d3-6e1b-4935-beb6-f11094f0334e · outbound

This paper cites Fine-grained temporal contrastive learning for weakly-supervised temporal action localization,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Fine-grained temporal contrastive learning for weakly-supervised temporal action localization,

Reference 29

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Observation 4877fea4-ddd2-4831-944a-5aabebace2e3 · outbound

This paper cites FineAction: A Fine-Grained Video Dataset for Temporal Action Localization.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA FineAction: A Fine-Grained Video Dataset for Temporal Action Localization

Reference 30

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Observation 556faa05-bdbb-494b-bd9f-b2e95e80c72d · outbound

This paper cites Fine-grained activity recognition in baseball videos,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Fine-grained activity recognition in baseball videos,

Reference 31

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Observation 9a3b178a-b543-4d13-84f9-d6264b60785c · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 32

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Observation 6f5c7f62-73e8-4bb2-bcaf-2c574e29b4fb · outbound

This paper cites Alleviating Over-segmentation Errors by Detecting Action Boundaries.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Alleviating Over-segmentation Errors by Detecting Action Boundaries

Reference 33

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Observation f55f16f9-97cc-4033-a323-e4294f93ead5 · outbound

This paper cites Learning and fusion of multiple hidden sub-phases for action quality assessment,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Learning and fusion of multiple hidden sub-phases for action quality assessment,

Reference 34

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Observation 32880c9e-2f46-496f-a884-82b81eefa9d9 · outbound

This paper cites Ms-tcn++: Multi-stage temporal convolutional network for action segmentation,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Ms-tcn++: Multi-stage temporal convolutional network for action segmentation,

Reference 35

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source=pdf_text observed=2026-08-03T23:48:41.507370Z digest=sha256:195abb713a6650ae5f174ae5a3cdde112591484d88eadde3276e3ea0930be802

Observation db594dea-4e43-4b22-b4a6-878ba93c77be · outbound

This paper cites Mtdan: A lightweight multi-scale temporal difference attention networks for automated video depression detection,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Mtdan: A lightweight multi-scale temporal difference attention networks for automated video depression detection,

Reference 36

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Observation 18d600ad-22c7-434e-a5f7-40b6157b6320 · outbound

This paper cites Bidirectional temporal and frame-segment attention for sparse action segmentation of figure skating,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Bidirectional temporal and frame-segment attention for sparse action segmentation of figure skating,

Reference 37

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Observation 35dba519-caa7-4e43-b89f-2de7e7da4055 · outbound

This paper cites On space-time interest points,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA On space-time interest points,

Reference 38

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Observation 4354fc3e-3375-4f3c-8e1b-be7184f9d8e7 · outbound

This paper cites Dense trajectories and motion boundary descriptors for action recognition,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Dense trajectories and motion boundary descriptors for action recognition,

Reference 39

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source=pdf_text observed=2026-08-03T23:48:42.065925Z digest=sha256:bdb91417bb063b7ea699c0974ee86a4e25711dfbce5ad42d513b856d060ba66c

Observation e130e983-aea7-46b7-9c82-04bf40aaffb5 · outbound

This paper cites Action recognition with improved trajectories,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Action recognition with improved trajectories,

Reference 40

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Observation fe9d3cf4-095a-4dd3-8fb6-f7433d9cf3c2 · outbound

This paper cites Beyond short snippets: Deep networks for video classification,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Beyond short snippets: Deep networks for video classification,

Reference 41

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source=pdf_text observed=2026-08-03T23:48:42.558949Z digest=sha256:bf4d8d89504b9c6927ae6efbb31eb3bf9630a813a6715b9d13e590778b703b01

Observation ad9fdef6-c51a-4ebc-a25e-6ffa4abe8c1c · outbound

This paper cites Unsupervised learning of video representations using lstms,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Unsupervised learning of video representations using lstms,

Reference 42

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source=pdf_text observed=2026-08-03T23:48:42.721053Z digest=sha256:7adf6144c45b053e9f976c21531b9f8082837b04ea2d73a6d1ea6bba112d9693

Observation 8cf9f985-b343-4f2c-8bb9-bee5f2bd5baf · outbound

This paper cites Long-term temporal convolutions for action recognition,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Long-term temporal convolutions for action recognition,

Reference 43

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Observation 4db22d5f-9141-471b-8931-4d25f264ce74 · outbound

This paper cites Action assessment by joint relation graphs,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Action assessment by joint relation graphs,

Reference 44

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source=pdf_text observed=2026-08-03T23:48:42.994886Z digest=sha256:36238163e908f2b2b649a02c08a0aa58fd30b3b3973140f16789c0441ff39236

Observation d8700d5c-5d58-4a20-b4f4-6ed45a6875ee · outbound

This paper cites Temporal segment networks: Towards good practices for deep action recognition,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Temporal segment networks: Towards good practices for deep action recognition,

Reference 45

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Observation 8b94d8e9-e2c4-4cc8-b5b8-68007d2fb4a6 · outbound

This paper cites Timemil: Advancing multivariate time series classification via a time-aware multiple instance learning,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Timemil: Advancing multivariate time series classification via a time-aware multiple instance learning,

Reference 46

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Observation 09a0e7ef-99bc-4e0b-aa98-6c34045990da · outbound

This paper cites Likert scoring with grade decoupling for long-term action assessment,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Likert scoring with grade decoupling for long-term action assessment,

Reference 47

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source=pdf_text observed=2026-08-03T23:48:43.369873Z digest=sha256:72678fa83d2ebc8f5058196fe3a9c131b1de421aae402f5b9772f703751063a2

Observation a89d0bcd-00eb-4029-9a85-64a11fdba215 · outbound

This paper cites Pairwise contrastive learning network for action quality assessment,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Pairwise contrastive learning network for action quality assessment,

Reference 48

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Observation 0a783a24-4535-4aac-bd78-d8fe4b5d2ec8 · outbound

This paper cites Auto-encoding score distribution regression for action quality assessment,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Auto-encoding score distribution regression for action quality assessment,

Reference 49

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Observation 9fd8b83e-dd22-40ce-94f2-1546f93115be · outbound

This paper cites Cofinal: Enhancing action quality assessment with coarse-to-fine instruction alignment,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Cofinal: Enhancing action quality assessment with coarse-to-fine instruction alignment,

Reference 50

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Observation fa9c9ec7-ae4f-4936-bdb6-2df27973b460 · outbound

This paper cites Asformer: Transformer for action segmentation,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Asformer: Transformer for action segmentation,

Reference 51

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Observation f4b6b71d-c4eb-4e73-9faa-eaef5a719ea4 · outbound

This paper cites Hierarchical neurosymbolic approach for comprehensive and explainable action quality assessment,.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Hierarchical neurosymbolic approach for comprehensive and explainable action quality assessment,

Reference 52

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Observation 187a38a0-1e47-4e45-a7bf-eaca070fa06e · outbound

This paper cites Out-of-sample generalizations for supervised manifold learning for classification.

Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA Out-of-sample generalizations for supervised manifold learning for classification

Reference 53

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Pith citing papers

Observation 7289f2d6-88e7-402e-ae1d-4a1d8d1ba9c5 · inbound

Again-Pose: Anchor-Guided Adaptive Inter-Frame Motion Cues Propagating for High-quality Human Pose Reconstruction cites this paper.

Again-Pose: Anchor-Guided Adaptive Inter-Frame Motion Cues Propagating for High-quality Human Pose Reconstruction Focus on What Matters: Constraining Spatial-Temporal Attention via Action-Units for Noise-Resilient AQA

Reference 46

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source=pdf_text observed=2026-06-30T07:44:24.069899Z digest=sha256:3da8bb09c0ab2e063ceeaf14d9215b782a71934c823ac5b2564295bf678d1a17