1,254 research outputs found
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.Comment: Accepted by AAAI202
SAR-NAS: Skeleton-based Action Recognition via Neural Architecture Searching
This paper presents a study of automatic design of neural network
architectures for skeleton-based action recognition. Specifically, we encode a
skeleton-based action instance into a tensor and carefully define a set of
operations to build two types of network cells: normal cells and reduction
cells. The recently developed DARTS (Differentiable Architecture Search) is
adopted to search for an effective network architecture that is built upon the
two types of cells. All operations are 2D based in order to reduce the overall
computation and search space. Experiments on the challenging NTU RGB+D and
Kinectics datasets have verified that most of the networks developed to date
for skeleton-based action recognition are likely not compact and efficient. The
proposed method provides an approach to search for such a compact network that
is able to achieve comparative or even better performance than the
state-of-the-art methods
Deep Learning on Lie Groups for Skeleton-based Action Recognition
In recent years, skeleton-based action recognition has become a popular 3D
classification problem. State-of-the-art methods typically first represent each
motion sequence as a high-dimensional trajectory on a Lie group with an
additional dynamic time warping, and then shallowly learn favorable Lie group
features. In this paper we incorporate the Lie group structure into a deep
network architecture to learn more appropriate Lie group features for 3D action
recognition. Within the network structure, we design rotation mapping layers to
transform the input Lie group features into desirable ones, which are aligned
better in the temporal domain. To reduce the high feature dimensionality, the
architecture is equipped with rotation pooling layers for the elements on the
Lie group. Furthermore, we propose a logarithm mapping layer to map the
resulting manifold data into a tangent space that facilitates the application
of regular output layers for the final classification. Evaluations of the
proposed network for standard 3D human action recognition datasets clearly
demonstrate its superiority over existing shallow Lie group feature learning
methods as well as most conventional deep learning methods.Comment: Accepted to CVPR 201
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