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Multi-scale structural graph convolutional network for skeleton-based action recognition
Graph convolutional networks (GCNs) have attracted considerable interest in skeleton-
based action recognition. Existing GCN-based models have proposed methods to learn …
based action recognition. Existing GCN-based models have proposed methods to learn …
Learning contrastive self-distillation for ultra-fine-grained visual categorization targeting limited samples
In the field of intelligent multimedia analysis, ultra-fine-grained visual categorization (Ultra-
FGVC) plays a vital role in distinguishing intricate subcategories within broader categories …
FGVC) plays a vital role in distinguishing intricate subcategories within broader categories …
Self-adaptive graph with nonlocal attention network for skeleton-based action recognition
Graph convolutional networks (GCNs) have achieved encouraging progress in modeling
human body skeletons as spatial–temporal graphs. However, existing methods still suffer …
human body skeletons as spatial–temporal graphs. However, existing methods still suffer …
Hierarchical aggregated graph neural network for skeleton-based action recognition
Supervised human action recognition methods based on skeleton data have achieved
impressive performance recently. However, many current works emphasize the design of …
impressive performance recently. However, many current works emphasize the design of …
MIGA-Net: Multi-view image information learning based on graph attention network for SAR target recognition
Neural networks for synthetic aperture radar (SAR) automatic target recognition often
encounter overfitting challenges owing to limited training samples. Moreover, the azimuth …
encounter overfitting challenges owing to limited training samples. Moreover, the azimuth …
Seeking False Hard Negatives for Graph Contrastive Learning
Graph Contrastive Learning (GCL) has achieved great success in self-supervised
representation learning throughout positive and negative pairs based on graph neural …
representation learning throughout positive and negative pairs based on graph neural …
Discriminative segment focus network for fine-grained video action recognition
Fine-grained video action recognition aims at identifying minor and discriminative variations
among fine categories of actions. While many recent action recognition methods have been …
among fine categories of actions. While many recent action recognition methods have been …
Skeleton-based action recognition through attention guided heterogeneous graph neural network
Previous graph convolutional networks typically use homogeneous graphs to explore the
hidden dependencies between joints in skeleton-based action recognition. Consequently …
hidden dependencies between joints in skeleton-based action recognition. Consequently …
Variation-aware directed graph convolutional networks for skeleton-based action recognition
Abstract Directed Graph convolutional networks (DGCNs) have been indeed gaining
attention and being applied in skeleton-based action recognition tasks to capture the …
attention and being applied in skeleton-based action recognition tasks to capture the …
DSDC-GCN: Decoupled Static-Dynamic Co-occurrence Graph Convolutional Networks for Skeleton-Based Action Recognition
The existing approaches for skeleton-based action recognition based on graph
convolutional networks (GCNs) primarily emphasize the construction of human skeletal …
convolutional networks (GCNs) primarily emphasize the construction of human skeletal …