Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T14:37:06.662946Z
Paper Citation Record · LEDGER
As of 13 August 2026, this Paper Citation Record lists 100 of 102 outbound references and 0 inbound Pith citation observations for arXiv:2412.11813.
A citation records a reference. It does not transfer a finding from one paper to another.
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Source: paper_references, paper_reference_links, observed 2026-08-11T14:37:06.662946Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
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Source: cited_works
100 of 102 outbound references displayed
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Observation d8d85645-2832-4ce9-85cd-e7de10a46528 · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Imagenet classification with deep convolutional neural networks
Reference 1
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Observation 7c75723d-a270-4f60-aa12-27d2da2661d6 · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work
Reference 2
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Semi-Supervised Classification with Graph Convolutional Networks
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Transductive kernel map learning and its application to image annotation
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Adaptive graph convolutional neural networks
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Directedacyclicgraphkernelsforactionrecognition
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition A new model for learning in graph domains
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Observation 17e02242-c15c-43a6-95dd-f73762af1f36 · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Robust face recognition using dynamic space warping
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Understanding attention and generalization in graph neural networks
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Learning attribute representations for remote sensing ship category classification
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Observation 6771b103-f3af-44ab-aa50-d1c5fac39e68 · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Improved knowledge distillation via teacher assistant
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition learning-compression
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Observation 956137ea-1f65-4fa7-8c90-85ae8ba5eb4a · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Relevance feedback for satellite image change detection
Reference 13
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Observation 4401171b-9cd5-45a2-bd34-6419fa1ab80e · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Morphnet: Fast & simple resource-constrained structure learning of deep networks
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Observation 6b4ecf9f-ad78-497c-9818-b561bb5c2db4 · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Sparse artificial neural networks using a novel smoothed lasso penalization
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Entropy-constrained training of deep neural networks
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Observation 8eb95570-ecf0-4b6b-9742-ac73d7a2917d · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Sahbi, J-Y
Reference 17
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Learning structured sparsity in deep neural networks
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Observation 17099dc2-6141-4f89-806b-ea4ed3a7595c · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Learning efficient convolutional networks through network slimming
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Observation b99f0a14-7288-499d-9016-7a64ac376ff7 · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Constrained optical flow for aerial image change detection
Reference 20
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Observation 76442ce5-981b-469f-9db6-42d891951950 · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Learning Sparse Neural Networks through $L_0$ Regularization
Reference 21
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Observation 64060b19-01c9-4118-b085-6797b9b13fcb · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Searching for mobilenetv3
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Wang and H
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Observation 83dec40e-78a0-4658-a4df-bb192810a9e8 · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Convolutional two-stream network fusion for video action recognition
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Observation a3db45aa-a491-45bb-85a6-8a8a596aa53e · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Transition forests: Learning discriminative temporal transitions for action recognition and detection
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Observation a3958c69-30b3-473c-9c76-3478e57f4ab8 · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Bourdis, D
Reference 26
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Observation 0630b98f-bd3d-4152-b4e2-6f8ba76b4ec2 · outbound
Designing Semi-Structured 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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Observation 0d911d56-8a47-4f0e-b9a7-cff45d346844 · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Mazari and H
Reference 28
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Mazari and H
Reference 29
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Observation 0678519f-321a-43e8-addb-a0dab4449f5f · outbound
Designing Semi-Structured 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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Observation 8abb41ef-7847-4f11-973a-fe824a599752 · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Learning both weights and connections for efficient neural network
Reference 31
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Observation 4550bde1-f5b2-4859-9a0b-fa59119c64b3 · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Nonlinear cross-view sample enrichment for action recognition
Reference 32
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Observation 9f4d8b71-dfdf-41b6-9bf7-c9faee20d7f6 · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Optimal brain damage.Advances in NIPS, 2, 1989
Reference 33
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Observation c7c27c4a-3595-4c96-9e11-163468ea380e · outbound
Designing Semi-Structured 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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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Coarse-to-fine deep kernel networks
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Jointly learning heterogeneous features for rgb-d activity recognition
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Spatio-temporal graph convolution for skeleton based action recognition
Reference 37
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Observation 16fbc6cd-8975-426a-aadf-13df38508a42 · outbound
Designing Semi-Structured 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
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Observation cbb86d61-0e54-4fd5-bff1-fac8181e07c7 · outbound
Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Laplacian deep kernel learning for image annotation
Reference 39
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Graph cnns with motif and variable temporal block for skeleton-based action recognition
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Topologically-consistentmagnitudepruningforverylightweightgraphconvolutionalnetworks
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Spatial temporal graph convolutional networks for skeleton-based action recognition
Reference 42
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition A riemannian network for spd matrix learning
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Sahbi and F
Reference 44
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Jiu and H
Reference 45
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Designing Semi-Structured 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
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Camera pose estimation using visual servoing for aerial video change detection
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition HAN: An Efficient Hierarchical Self-Attention Network for Skeleton-Based Gesture Recognition
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Building deep networks on grassmann manifolds
Reference 50
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Decoupled representation learning for skeleton-based gesture recognition
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Adam: A Method for Stochastic Optimization
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Structured pruning of neural networks with budget-aware regularization
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Bags-of-daglets for action recognition
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Pruning Filters for Efficient ConvNets
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work
Reference 57
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Hierarchical recurrent neural network for skeleton based action recognition
Reference 58
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Spatio-temporal lstm with trust gates for 3d human action recognition
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Interactive satellite image change detection with context-aware canonical correlation analysis
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Designing Semi-Structured 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
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition View adaptive recurrent neural networks for high performance human action recognition from skeleton data
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Deepgru: Deep gesture recognition utility
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Rgb-d-based human motion recognition with deep learning: A survey.CVIU, 2018
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Linear-time online action detection from 3d skeletal data using bags of gesturelets
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work
Reference 69
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Applying interest operators in semi-fragile video watermarking
Reference 70
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition DropNeuron: Simplifying the Structure of Deep Neural Networks
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Using entropy for image and video authentication watermarks
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition An end-to-end spatio-temporal attention model for human action recognition from skeleton data
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Human action recognition by representing 3d skeletons as points in a lie group
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition From coarse to fine skin and face detection
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Regularization of neural networks using dropconnect
Reference 76
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Designing Semi-Structured 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
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Yuan, G-S
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Interactive body part contrast mining for human interaction recognition
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Category-blind human action recognition: A practical recognition system
Reference 80
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Sahbi and F
Reference 81
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Hon4d: Histogram of oriented 4d normals for activity recognition from depth sequences
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition 3d action recognition from novel viewpoints
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Sahbi and D
Reference 84
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Two-person interaction detection using body-pose features and multiple instance learning
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Jiu and H
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition The moving pose: An efficient 3d kinematics descriptor for low-latency action recognition and detection
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Efficient temporal sequence comparison and classification using gram matrix embeddings on a riemannian manifold
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Context-dependent kernel design for object matching and recognition
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Designing Semi-Structured 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
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition TELECOM ParisTech at ImageClefphoto 2008: Bi-Modal Text and Image Retrieval with Diversity Enhancement
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Coarse-to-fine support vector classifiers for face detection
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Misalignment resilient cca for interactive satellite image change detection
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Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Semi supervised deep kernel design for image annotation
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