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Paper Citation Record · LEDGER

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition

As of 13 August 2026, this Paper Citation Record lists 100 of 102 outbound references and 0 inbound Pith citation observations for arXiv:2412.12887.

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pith.paper-citation-record.v1
2412.12887 v1

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measured 100 of 102 reference resolution

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measured 100 of 100 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Reference resolution

100 of 102 outbound references displayed

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Outbound references

Observation f61d1729-1092-4fe9-9d95-ba6fd52f5e1e · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Imagenet classification with deep convolutional neural networks

Reference 1

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Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work

Reference 2

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This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Semi-Supervised Classification with Graph Convolutional Networks

Reference 3

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This paper cites Transductive kernel map learning and its application to image annotation.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Transductive kernel map learning and its application to image annotation

Reference 4

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This paper cites Adaptive graph convolutional neural networks.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Adaptive graph convolutional neural networks

Reference 5

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This paper cites Directedacyclicgraphkernelsforactionrecognition.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Directedacyclicgraphkernelsforactionrecognition

Reference 6

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This paper cites A new model for learning in graph domains.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition A new model for learning in graph domains

Reference 7

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This paper cites Robust face recognition using dynamic space warping.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Robust face recognition using dynamic space warping

Reference 8

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Observation 962344b0-04cc-42fa-a048-99203dd97c27 · outbound

This paper cites Understanding attention and generalization in graph neural networks.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Understanding attention and generalization in graph neural networks

Reference 9

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This paper cites Learning attribute representations for remote sensing ship category classification.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Learning attribute representations for remote sensing ship category classification

Reference 10

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This paper cites Improved knowledge distillation via teacher assistant.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Improved knowledge distillation via teacher assistant

Reference 11

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Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition learning-compression

Reference 12

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This paper cites Relevance feedback for satellite image change detection.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Relevance feedback for satellite image change detection

Reference 13

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This paper cites Morphnet: Fast & simple resource-constrained structure learning of deep networks.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Morphnet: Fast & simple resource-constrained structure learning of deep networks

Reference 14

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This paper cites Sparse artificial neural networks using a novel smoothed lasso penalization.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Sparse artificial neural networks using a novel smoothed lasso penalization

Reference 15

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Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Entropy-constrained training of deep neural networks

Reference 16

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Observation 39f58eb9-92a4-4334-9929-33ec4fdea0e0 · outbound

This paper cites Sahbi, J-Y.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Sahbi, J-Y

Reference 17

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Observation 1617eb73-de33-456b-af3f-d6840ed15360 · outbound

This paper cites Learning structured sparsity in deep neural networks.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Learning structured sparsity in deep neural networks

Reference 18

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This paper cites Learning efficient convolutional networks through network slimming.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Learning efficient convolutional networks through network slimming

Reference 19

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This paper cites Constrained optical flow for aerial image change detection.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Constrained optical flow for aerial image change detection

Reference 20

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This paper cites Learning Sparse Neural Networks through $L_0$ Regularization.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Learning Sparse Neural Networks through $L_0$ Regularization

Reference 21

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This paper cites Searching for mobilenetv3.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Searching for mobilenetv3

Reference 22

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This paper cites Wang and H.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Wang and H

Reference 23

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This paper cites Convolutional two-stream network fusion for video action recognition.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Convolutional two-stream network fusion for video action recognition

Reference 24

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Observation 6a38e465-32d4-4561-8bd8-940eacfa8772 · outbound

This paper cites Transition forests: Learning discriminative temporal transitions for action recognition and detection.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Transition forests: Learning discriminative temporal transitions for action recognition and detection

Reference 25

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Observation f51ec977-9a14-4e01-8bb6-ec9ae5656634 · outbound

This paper cites Bourdis, D.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Bourdis, D

Reference 26

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Observation fcc6f851-3215-410c-9d9a-03df80a66eed · outbound

This paper cites First-person hand action benchmark with rgb-d videos and 3d hand pose annotations.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition First-person hand action benchmark with rgb-d videos and 3d hand pose annotations

Reference 27

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This paper cites Mazari and H.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Mazari and H

Reference 28

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This paper cites Mazari and H.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Mazari and H

Reference 29

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This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 30

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This paper cites Learning both weights and connections for efficient neural network.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Learning both weights and connections for efficient neural network

Reference 31

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This paper cites Nonlinear cross-view sample enrichment for action recognition.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Nonlinear cross-view sample enrichment for action recognition

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Observation 0f59b860-a72e-4f54-bb5e-9a404b9e3855 · outbound

This paper cites Optimal brain damage.Advances in NIPS, 2, 1989.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Optimal brain damage.Advances in NIPS, 2, 1989

Reference 33

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Observation c6b88aaa-2155-4bf7-abb1-ca37a8c8dde6 · outbound

This paper cites Second order derivatives for network pruning: Optimal brain surgeon.Advances in NIPS, 5, 1992.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Second order derivatives for network pruning: Optimal brain surgeon.Advances in NIPS, 5, 1992

Reference 34

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Observation f671cbf0-c5dd-4780-b388-531588dc9b1f · outbound

This paper cites Coarse-to-fine deep kernel networks.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Coarse-to-fine deep kernel networks

Reference 35

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Observation dc941d92-989d-4e63-97a8-eefb3e2ac97f · outbound

This paper cites Jointly learning heterogeneous features for rgb-d activity recognition.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Jointly learning heterogeneous features for rgb-d activity recognition

Reference 36

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Observation 4af4b3c1-73cf-4a85-93a5-26470cef6070 · outbound

This paper cites Spatio-temporal graph convolution for skeleton based action recognition.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Spatio-temporal graph convolution for skeleton based action recognition

Reference 37

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verified fuzzy
raw_fallback, observed 2026-08-11T13:42:52.318311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.004014Z digest=sha256:12ad968800b5ea5720fff2dffa7171a707bca3b93f301954ec3ad6638d1cbf5b

Observation 934ffa02-e00b-4b9c-8b8a-63cfcd81809e · outbound

This paper cites Global co-occurrence feature learning and active coordinate system conversion for skeleton-based action recognition.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Global co-occurrence feature learning and active coordinate system conversion for skeleton-based action recognition

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:52.304242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.009345Z digest=sha256:38841dfc1efcdd76e1d67513bed4486f3ee7c6872231bb6d2ed80691070db67d

Observation 6afa5445-aa74-45ef-8618-6572b20e5778 · outbound

This paper cites Laplacian deep kernel learning for image annotation.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Laplacian deep kernel learning for image annotation

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:52.288811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.014018Z digest=sha256:e3337d43aacb97f23c177d97f1fc18bc728a0cf0f469b4891af435115b7f77a0

Observation 101e200e-ee54-4f72-8fc1-62767868ff4c · outbound

This paper cites Graph cnns with motif and variable temporal block for skeleton-based action recognition.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Graph cnns with motif and variable temporal block for skeleton-based action recognition

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:52.275638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.020529Z digest=sha256:914cc3ac7da7cdb757bd12867b38d2a34f10b677c297479f30439be5f6c0c9cd

Observation 44de6d50-f4eb-4b8b-a13e-0cc664c879b9 · outbound

This paper cites Topologically-consistentmagnitudepruningforverylightweightgraphconvolutionalnetworks.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Topologically-consistentmagnitudepruningforverylightweightgraphconvolutionalnetworks

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:52.259934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.025795Z digest=sha256:e1adccfa067db7fd67226b83c981ecbfc4a78ea0a85c4edca262541decfdc007

Observation b4d0f05d-2c78-46b4-bb76-7bc8d8442a6d · outbound

This paper cites Spatial temporal graph convolutional networks for skeleton-based action recognition.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Spatial temporal graph convolutional networks for skeleton-based action recognition

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:52.243956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.031390Z digest=sha256:8f91aabd5403a965e32c751e242d80a8932dd3cef58b15cb590da6aae22992b9

Observation 6cad3fa5-8ea1-4c8d-904a-2ba0321c29db · outbound

This paper cites A riemannian network for spd matrix learning.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition A riemannian network for spd matrix learning

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:52.230150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.036511Z digest=sha256:829b7ab44f78ef5f92f414b3627646f5e852a2805b217617fadd5ddb71c3cda5

Observation 35a6c0eb-2f18-4cf7-84e0-effdfe14395d · outbound

This paper cites Sahbi and F.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Sahbi and F

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.041365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.041365Z digest=sha256:79fc30fdc637e22f949178eb84bf255f0be0d90e40af42fbfed749eb9601e437

Observation f05cdaf4-7501-4f43-9348-0d93573e6e85 · outbound

This paper cites Jiu and H.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Jiu and H

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:52.209101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.046670Z digest=sha256:fcdc84f1df124fff3397a91d34e87f0904278f13351d985c9df795cfc5aa9f49

Observation a7d17a76-73ca-46e0-8606-4b24b094ee49 · outbound

This paper cites A novel geometric framework on gram matrix trajectories for human behavior understanding.IEEE TPAMI, 42(1):1–14, 2018.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition A novel geometric framework on gram matrix trajectories for human behavior understanding.IEEE TPAMI, 42(1):1–14, 2018

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:52.196625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.055198Z digest=sha256:7cf76dd5c2782a356a166c58c8f309ce37dcc1bd3e9a3d52ad81927d01bd95f6

Observation 5a6ded20-8bdf-43bf-92b1-d34d6cbc084a · outbound

This paper cites Camera pose estimation using visual servoing for aerial video change detection.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Camera pose estimation using visual servoing for aerial video change detection

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.064598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.064598Z digest=sha256:d34a2693dcec5e8993b034c348b8113477bb131abbd3d0179f425c81f2a0eab8

Observation e883fadd-a996-4ce6-ab81-97aeb7ebbd24 · outbound

This paper cites an unresolved cited work.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.070641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.070641Z digest=sha256:add2f0298ee660b6fbddb1eef4665e5b35b59ae8ec37c664c3321ce5d11b6140

Observation 1d22be74-2827-45b6-b70b-bf93e7bd6c92 · outbound

This paper cites HAN: An Efficient Hierarchical Self-Attention Network for Skeleton-Based Gesture Recognition.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition HAN: An Efficient Hierarchical Self-Attention Network for Skeleton-Based Gesture Recognition

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.075466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.075466Z digest=sha256:3b3fb912e43addd95911efc1f93dd6e9f9a981cb69c8b7aff823eaa0c2c8bf82

Observation 07d4c983-41e4-4b2b-b515-ac356127cff9 · outbound

This paper cites Building deep networks on grassmann manifolds.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Building deep networks on grassmann manifolds

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:52.164957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.080905Z digest=sha256:6ed7306e75968a28061c84171353a45ff6453009ea5eaf1e821c569737ac8aaf

Observation 0881a5a2-891f-41bc-a9af-2ea3f6f5858e · outbound

This paper cites Decoupled representation learning for skeleton-based gesture recognition.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Decoupled representation learning for skeleton-based gesture recognition

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:52.147661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.084963Z digest=sha256:d83a8a26fb17e0e2947c7e131f215812dd3c1773d65be5835ab6de1005d998f5

Observation 8aa5e3ee-63b5-43e8-91d2-ffadfbd38d97 · outbound

This paper cites an unresolved cited work.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work

Reference 52

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:42:52.131364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.089622Z digest=sha256:d75d6bea12b5b1e3a804b37d364358cf446318a1734ca94416d7d7d5a1181578

Observation 81824da5-fe42-4f51-88ed-a50e9bd080a5 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Adam: A Method for Stochastic Optimization

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.094003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.094003Z digest=sha256:f52b4ba17e2f810f02e5432bb58a8b4f6dab7d91eacc842d16a470c64c6e09f9

Observation a58ade37-b486-47a6-909c-66205773b5de · outbound

This paper cites Structured pruning of neural networks with budget-aware regularization.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Structured pruning of neural networks with budget-aware regularization

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:52.116301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.099253Z digest=sha256:cfc243239d8c5d88a7c056f9353c908d5a7b9deeac61ed950c46cd639c3440f2

Observation 9e3fa9f5-bb55-494e-992e-134ba9d04029 · outbound

This paper cites Bags-of-daglets for action recognition.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Bags-of-daglets for action recognition

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.103691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.103691Z digest=sha256:84a15742516e171a0070e241b01f5e45d5eef7692abb8f983f8f2ad03765411a

Observation 662a721b-e088-4d2c-9eed-1ab0434a92b0 · outbound

This paper cites Pruning Filters for Efficient ConvNets.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Pruning Filters for Efficient ConvNets

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.107555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.107555Z digest=sha256:feae389a81edd998cd9a13d80c5f158943fac6fabfa66655fa6368fe60d70d2b

Observation 3553d56c-eae3-4ab2-ac53-061d6ec042b2 · outbound

This paper cites an unresolved cited work.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work

Reference 57

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:42:52.089129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.112057Z digest=sha256:434045a13e23b3d3db8bee23fd71887ca830386292a80a4d360890db82c345b5

Observation 6d198829-7c24-46b2-9c06-094973a78d32 · outbound

This paper cites Hierarchical recurrent neural network for skeleton based action recognition.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Hierarchical recurrent neural network for skeleton based action recognition

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:52.076516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.115757Z digest=sha256:3267dfcc2c4f628e9beb9ab20980bca0782a9de2579179b083f01d0a00fb16e4

Observation 91ca6504-5e92-441f-bcbf-6687b715bcfd · outbound

This paper cites Spatio-temporal lstm with trust gates for 3d human action recognition.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Spatio-temporal lstm with trust gates for 3d human action recognition

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:52.059577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.119503Z digest=sha256:32a0aaf148b9a6720c40cd7df847dd66862d2ce235d91fbdadf02e774b368aba

Observation 16f14cc2-c6b3-4d8a-ba2d-3539052a49e7 · outbound

This paper cites Interactive satellite image change detection with context-aware canonical correlation analysis.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Interactive satellite image change detection with context-aware canonical correlation analysis

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.123782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.123782Z digest=sha256:85d67ceda0e04d8466f8c9826305575dd859689dc620240700cc83775e636492

Observation 6144d93f-8b78-487c-b439-a4e05b7af913 · outbound

This paper cites Skeleton-based human action recognition with global context-aware attention lstm networks.IEEE Transactions on Image Processing, 27(4):1586–1599, 2017.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Skeleton-based human action recognition with global context-aware attention lstm networks.IEEE Transactions on Image Processing, 27(4):1586–1599, 2017

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:52.034957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.127977Z digest=sha256:48c8e349b485e86f8fd8c8c873a2fd1c079a4a32b94aaf39a8cfe5fbd5b6f223

Observation dd7008b1-6a36-4ddc-99f3-af1242c51ca8 · outbound

This paper cites View adaptive recurrent neural networks for high performance human action recognition from skeleton data.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition View adaptive recurrent neural networks for high performance human action recognition from skeleton data

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:52.021357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.131866Z digest=sha256:6c6e34f97688d65cc96c9b2cbd85490e75887c601e22db613ed0cf6ea6367a17

Observation 8a7f96bf-821d-4c0a-977e-5e0ce4621f89 · outbound

This paper cites an unresolved cited work.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work

Reference 63

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:42:51.998663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.136066Z digest=sha256:dc4531b2b34203176bef053ea62ad434651560a683304fb2ce36547c147734de

Observation f980714d-7803-4428-a2fa-5801865b4a81 · outbound

This paper cites Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.986010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.140729Z digest=sha256:6fb7926539275b8cb92fa433302cc0300a9c70b4ea73b2b1d8443ebce51719f2

Observation a938c34c-01de-436b-81a5-2357990c63b7 · outbound

This paper cites Deepgru: Deep gesture recognition utility.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Deepgru: Deep gesture recognition utility

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.973562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.144870Z digest=sha256:67c647f52ef0a3455cfcbd8baa74411c75efdf5673688a2d2ee3a7ed5e5ce307

Observation 2e073a43-90df-49c2-b631-23ad67be964c · outbound

This paper cites Rgb-d-based human motion recognition with deep learning: A survey.CVIU, 2018.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Rgb-d-based human motion recognition with deep learning: A survey.CVIU, 2018

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.961216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.149046Z digest=sha256:3fb20e9bd8181eae3d29631bf24aaa3d17d9c13fcf9479d391efc8d029870016

Observation 0ec343fc-2726-4e14-bbdb-e8f202222190 · outbound

This paper cites an unresolved cited work.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work

Reference 67

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:42:51.948366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.153475Z digest=sha256:0fa67e90a9f4749eda0a6ff7ddba27582546e98bc6df3fe45c49005126de1c01

Observation 2784a817-fa9a-4c88-b433-93bb85b39009 · outbound

This paper cites Linear-time online action detection from 3d skeletal data using bags of gesturelets.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Linear-time online action detection from 3d skeletal data using bags of gesturelets

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.937374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.157306Z digest=sha256:427d1efda2a5c46ec5a4765627bfddac1c6279523f5294568b343574f933b1a7

Observation 8cb8013f-2563-444d-93e8-a52461ba7f78 · outbound

This paper cites an unresolved cited work.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work

Reference 69

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:42:51.925731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.161864Z digest=sha256:92a37f59834a54290a1bf718b7ea28373cf8af433b304fd089ac2613710c2df4

Observation a22d7d64-8440-41ad-bc01-aebafb881042 · outbound

This paper cites Applying interest operators in semi-fragile video watermarking.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Applying interest operators in semi-fragile video watermarking

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.166334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.166334Z digest=sha256:68d282d561eefd57d615c98c2eb6848a56e18e1955a3af2822fd41b4883fa60e

Observation b660e041-3328-46f5-a9b3-bc5f1a5ec5bd · outbound

This paper cites DropNeuron: Simplifying the Structure of Deep Neural Networks.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition DropNeuron: Simplifying the Structure of Deep Neural Networks

Reference 71

Resolution
verified exact
local_arxiv, observed 2026-08-11T13:42:51.396440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.172417Z digest=sha256:4742ed74e8e1cfd2505487a95ad19a7c69bdbe8f6bc948106024c8e0154d6b67

Observation 4ab16360-47a2-4f83-9aa5-4235f32149a6 · outbound

This paper cites Using entropy for image and video authentication watermarks.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Using entropy for image and video authentication watermarks

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.178174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.178174Z digest=sha256:42307b38b990640090f89c4f7d19c4bc474e9a114da6d9e8b513d33caeb56649

Observation 2a705847-342e-4504-bb68-93e6fc1d7eee · outbound

This paper cites An end-to-end spatio-temporal attention model for human action recognition from skeleton data.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition An end-to-end spatio-temporal attention model for human action recognition from skeleton data

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.898353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.182469Z digest=sha256:f4eea2ac34c1de811328eb50a4f9f4a7b5dcec941e98cc9cb57cb89030a7aa4d

Observation a490e949-1639-4ba6-b707-c7727b24a72e · outbound

This paper cites Human action recognition by representing 3d skeletons as points in a lie group.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Human action recognition by representing 3d skeletons as points in a lie group

Reference 74

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verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.886019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.186465Z digest=sha256:23a1721f8032ae32ae39c8ac7ad0b6e70bfefde2dc01db9a19fd79815040d158

Observation 95fe2bfa-8754-44e8-ab44-e1d551752d8b · outbound

This paper cites From coarse to fine skin and face detection.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition From coarse to fine skin and face detection

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.190348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.190348Z digest=sha256:3e32fa73b0e001c6a7deb2f126e4edd0b56a54bc7fba06d65b6ba896268be369

Observation 1c404aa0-8090-43da-aa7f-e74dfa382ddb · outbound

This paper cites Regularization of neural networks using dropconnect.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Regularization of neural networks using dropconnect

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.866987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.194255Z digest=sha256:7cbb3187ace26bb64a635ba63be5828e1dc68ae3784b2c7950731aa121542cc3

Observation eee49589-3d07-4425-afc0-4de95e3b4dc6 · outbound

This paper cites Effective 3d action recognition using eigenjoints.Journal of Visual Communication and Image Representation, 25(1):2–11, 2014.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Effective 3d action recognition using eigenjoints.Journal of Visual Communication and Image Representation, 25(1):2–11, 2014

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.854831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.199603Z digest=sha256:f483143e60a0db9c7ad4472338dae8ba08180fde416720038ab2667d9b05cb77

Observation 27aba95d-b6f4-4a2f-b9b2-bc90a85c9991 · outbound

This paper cites Yuan, G-S.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Yuan, G-S

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.841396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.204553Z digest=sha256:9793c08c8de1de1f82423eb9090db8c74f1f60148a1119384e00e939da282c91

Observation 22eae215-50c0-4344-83d8-e0c444205a82 · outbound

This paper cites Interactive body part contrast mining for human interaction recognition.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Interactive body part contrast mining for human interaction recognition

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.817674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.210414Z digest=sha256:054abe726bf8cfa06a7482fd516aca45459f901e2e5300561556fbbf26635f5a

Observation 277b4d4f-9e25-4590-adf8-6b82dac7b4ee · outbound

This paper cites Category-blind human action recognition: A practical recognition system.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Category-blind human action recognition: A practical recognition system

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.804274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.214518Z digest=sha256:31f756dd2b36b06de5433b2b6b38a6b518525796ce19464a643b65189d4a2941

Observation 9bd8b3cf-16b2-4f8a-9881-9754496b4a3c · outbound

This paper cites Sahbi and F.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Sahbi and F

Reference 81

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.788970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.219987Z digest=sha256:17c3a61b5e2ce4a2f3a7379fea803d782fec75c55e77693125596c996cf459df

Observation 5c793d64-c0f0-49f7-9b3c-99b3dd690b7f · outbound

This paper cites Hon4d: Histogram of oriented 4d normals for activity recognition from depth sequences.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Hon4d: Histogram of oriented 4d normals for activity recognition from depth sequences

Reference 82

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.775493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.226651Z digest=sha256:840444c1e6d2c3a94b1b741d50bbe4136cf710b795f02175b962ca21ff29c2ac

Observation d705ab9c-374d-4059-9cb0-44ca6237e5c2 · outbound

This paper cites 3d action recognition from novel viewpoints.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition 3d action recognition from novel viewpoints

Reference 83

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.761266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.232448Z digest=sha256:b8580c15fd0714769e2d5a1af35c8f3fab2d26554636808d97a4c999f7d0eb47

Observation c6877643-3203-469f-8c5f-227c979451eb · outbound

This paper cites Sahbi and D.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Sahbi and D

Reference 84

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.740367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.241583Z digest=sha256:0bedd5ad387d4b1b0e46ee9c28feaf4369eb0a19df7ec05df142970add365a49

Observation 6dbb1823-21f4-4d4d-a246-d3891f92d29c · outbound

This paper cites Two-person interaction detection using body-pose features and multiple instance learning.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Two-person interaction detection using body-pose features and multiple instance learning

Reference 85

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.727516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.247067Z digest=sha256:3e8cc0e3d5153202a8fa2b2a42802282dd1f79336548986916f0126976b50e45

Observation e2463a14-85fd-4a99-aa95-8c3596ab01fa · outbound

This paper cites Jiu and H.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Jiu and H

Reference 86

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.712484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.252258Z digest=sha256:270934022ddd90990103f54f7a21341177855c84852ebcc18ca14c0e695e54dc

Observation 46fa4716-b9ab-4573-8ce6-cd44b5481c89 · outbound

This paper cites The moving pose: An efficient 3d kinematics descriptor for low-latency action recognition and detection.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition The moving pose: An efficient 3d kinematics descriptor for low-latency action recognition and detection

Reference 87

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.695792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.256588Z digest=sha256:73f87557c768e1f89fab9c8ebef00ef9cff94e50f1e7b5b0248380453c65d583

Observation fa9b3a48-8b6f-4db2-86b1-9b5742eab70a · outbound

This paper cites Graph-cut transducers for relevance feedback in content based image retrieval.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Graph-cut transducers for relevance feedback in content based image retrieval

Reference 88

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.264048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.264048Z digest=sha256:c7ed399ac6a397c9ed56deda519ce96f3084174353fcbc4796e6ffc11da45f20

Observation 3e3431e6-4265-4f8c-b720-e21d8f95c844 · outbound

This paper cites Efficient temporal sequence comparison and classification using gram matrix embeddings on a riemannian manifold.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Efficient temporal sequence comparison and classification using gram matrix embeddings on a riemannian manifold

Reference 89

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.669450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.271750Z digest=sha256:47669581ebf53ce2ca67ad6d1d51ee2c1f55c0bfc56d69b04ca1d1a5ddd0f0d5

Observation 05e02790-494f-4b74-b1a7-9993ea572cec · outbound

This paper cites Context-dependent kernel design for object matching and recognition.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Context-dependent kernel design for object matching and recognition

Reference 90

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.276776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.276776Z digest=sha256:feebc6dbfb61a67427b03c777635d464213496e882badf47d1db95298850b9db

Observation 17d08ef4-9e15-4b83-b973-dd7278903917 · outbound

This paper cites Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition.Pattern Recognition, 76:80–94, 2018.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition.Pattern Recognition, 76:80–94, 2018

Reference 91

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.648103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.281583Z digest=sha256:255e7dc10a56eed78e53bd35ddf5fb729247f61fc0d883d26673d3b7a550fcf1

Observation 2be5c6be-c4e3-4c46-acc9-99166cfde2dd · outbound

This paper cites Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization

Reference 92

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.285314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.285314Z digest=sha256:ab12d419b8bce6e4ce3ebaebc959416cde89d06b058fc844b54adc9ff4400b15

Observation 44207c82-e248-438a-b8ce-7fac74403ec9 · outbound

This paper cites an unresolved cited work.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work

Reference 93

Resolution
unresolved
raw_fallback, observed 2026-08-11T13:42:51.634077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.289239Z digest=sha256:94a34ac2fb653be4301223335bd093fc2c1cf56036365407d81c2d6809115aac

Observation 0b7253e6-972f-4fd1-a659-f8c2c7d8b778 · outbound

This paper cites TELECOM ParisTech at ImageClefphoto 2008: Bi-Modal Text and Image Retrieval with Diversity Enhancement.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition TELECOM ParisTech at ImageClefphoto 2008: Bi-Modal Text and Image Retrieval with Diversity Enhancement

Reference 94

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no resolver link, observed 2026-08-11T13:42:51.293443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.293443Z digest=sha256:0b24e900a86c3f21c4416872d2368787445847a99c5552605aa0dc8733cf3fea

Observation bb0c814a-d676-4f9e-beda-2e43b54bd0e4 · outbound

This paper cites CNRS-TELECOM ParisTech at ImageCLEF 2013 Scalable Concept Image Annotation Task: Winning Annotations with Context Dependent SVMs.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition CNRS-TELECOM ParisTech at ImageCLEF 2013 Scalable Concept Image Annotation Task: Winning Annotations with Context Dependent SVMs

Reference 95

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.297463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.297463Z digest=sha256:c2c6855284f5acdcc0a3116c7a6213fed6893f50447c6fe979a7a6aed172d926

Observation 17e51e07-f573-43bd-9f22-4b9ce2dd6c1f · outbound

This paper cites Coarse-to-fine support vector classifiers for face detection.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Coarse-to-fine support vector classifiers for face detection

Reference 96

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.301488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.301488Z digest=sha256:d6ac2aacaec9ee4b44f51eb976d266da6bef3999cbc1e170ce1914d31b927538

Observation fa1d92ec-3603-4cba-ab5e-64e8eeaaedc9 · outbound

This paper cites Visual content extraction for automatic semantic annotation of video news.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Visual content extraction for automatic semantic annotation of video news

Reference 97

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:42:51.594593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:42:51.305302Z digest=sha256:6d818603fdedbe280c031cb3a06931fc0bdfe92186aa50698943c0465ddd4042

Observation d4fb27aa-75e4-4183-93f0-2d2ef6399fbd · outbound

This paper cites Misalignment resilient cca for interactive satellite image change detection.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Misalignment resilient cca for interactive satellite image change detection

Reference 98

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.309222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.309222Z digest=sha256:166da7cb4371e9baf9212e55d4832b28cb5b30200a9fe180b0c48e6af48db463

Observation 7580bfda-1df8-474f-90e6-ca1e61f08870 · outbound

This paper cites From 2D silhouettes to 3D object retrieval: contributions and benchmarking.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition From 2D silhouettes to 3D object retrieval: contributions and benchmarking

Reference 99

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.312844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.312844Z digest=sha256:41401d0a905445bd018dc6a2881fe59b51350a62785c347a2363bf5251b44ecc

Observation b6cff1d4-1833-4d0a-a73c-9bf65fe88027 · outbound

This paper cites Semi supervised deep kernel design for image annotation.

Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition Semi supervised deep kernel design for image annotation

Reference 100

Resolution
unresolved
no resolver link, observed 2026-08-11T13:42:51.317022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:42:51.317022Z digest=sha256:f2a0d3bd79475512cf7ce870d239127de18f4e584350bc0de2e98d5ccc8bc684

Pith citing papers

No inbound Pith citation observations are available.