Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching
Human action recognition from skeleton data, fueled by the Graph Convolutional Network (GCN), has attracted lots of attention, due to its powerful capability of modeling non-Euclidean structure data. However, many existing GCN methods provide a pre-defined graph and fix it through the entire network, which can loss implicit joint correlations. Besides, the mainstream spectral GCN is approximated by one-order hop, thus higher-order connections are not well involved. Therefore, huge efforts are required to explore a better GCN architecture. To address these problems, we turn to Neural Architecture Search (NAS) and propose the first automatically designed GCN for skeleton-based action recognition. Specifically, we enrich the search space by providing multiple dynamic graph modules after fully exploring the spatial-temporal correlations between nodes. Besides, we introduce multiple-hop modules and expect to break the limitation of representational capacity caused by one-order approximation. Moreover, a sampling- and memory-efficient evolution strategy is proposed to search an optimal architecture for this task. The resulted architecture proves the effectiveness of the higher-order approximation and the dynamic graph modeling mechanism with temporal interactions, which is barely discussed before. To evaluate the performance of the searched model, we conduct extensive experiments on two very large scaled datasets and the results show that our model gets the state-of-the-art results.
Code (1)
Tasks
Action RecognitionNeural Architecture SearchSkeleton Based Action RecognitionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Graph Edge Convolutional Neural Networks for Skeleton Based Action Recognition
This paper investigates body bones from skeleton data for skeleton based action recognition. Body joints, as the direct result of mature pose estimation technologies, are always the key concerns of traditional action rec…
Action RecognitionPose EstimationSkeleton Based Action RecognitionTemporal Action LocalizationUnifying Graph Embedding Features with Graph Convolutional Networks for Skeleton-based Action Recognition
Combining skeleton structure with graph convolutional networks has achieved remarkable performance in human action recognition. Since current research focuses on designing basic graph for representing skeleton data, thes…
Action RecognitionGraph EmbeddingSkeleton Based Action RecognitionTemporal Action LocalizationProgressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognition
Graph convolutional networks (GCNs) have been very successful in skeleton-based human action recognition where the sequence of skeletons is modeled as a graph. However, most of the GCN-based methods in this area train a …
Action RecognitionTemporal Action LocalizationPart-based Graph Convolutional Network for Action Recognition
Human actions comprise of joint motion of articulated body parts or `gestures'. Human skeleton is intuitively represented as a sparse graph with joints as nodes and natural connections between them as edges. Graph convol…
Action RecognitionSkeleton Based Action RecognitionTemporal Action LocalizationPose-Guided Graph Convolutional Networks for Skeleton-Based Action Recognition
Graph convolutional networks (GCNs), which can model the human body skeletons as spatial and temporal graphs, have shown remarkable potential in skeleton-based action recognition. However, in the existing GCN-based metho…
Action RecognitionSkeleton Based Action RecognitionTemporal Action Localization