Skeleton-Based Human Action Recognition Based on Single Path One-Shot Neural Architecture Search

Abstract

Skeleton-based human action recognition based on Neural Architecture Search (NAS.) adopts a one-shot NAS strategy. It improves the speed of evaluating candidate models in the search space through weight sharing, which has attracted significant attention. However, directly applying the one-shot NAS method for skeleton recognition requires training a super-net with a large search space that traverses various combinations of model parameters, which often leads to overly large network models and high computational costs. In addition, when training this super-net, the one-shot NAS needs to traverse the entire search space of the complete skeleton recognition task. Furthermore, the traditional method does not consider the optimization of the search strategy. As a result, a significant amount of search time is required to obtain a better skeleton recognition network model. A more efficient weighting model, a NAS skeleton recognition model based on the Single Path One-shot (SNAS-GCN) strategy, is proposed to address the above challenges. First, to reduce the model search space, a simplified four-category search space is introduced to replace the mainstream multi-category search space. Second, to improve the model search efficiency, a single-path one-shot approach is introduced, through which the model randomly samples one architecture at each step of the search training optimization. Finally, an adaptive Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is proposed to obtain a candidate structure of the perfect model automatically. With these three steps, the entire network architecture of the recognition model (and its weights) is fully and equally trained significantly. The search and training costs will be greatly reduced. The search-out model is trained by the NTU-RGB + D and Kinetics datasets to evaluate the performance of the proposed model’s search strategy. The experimental results show that the search time of the proposed method in this paper is 0.3 times longer than that of the state-of-the-art method. Meanwhile, the recognition accuracy is roughly comparable compared to that of the SOTA NAS-GCN method.

AutoGCN-Toward Generic Human Activity Recognition With Neural Architecture Search

“…Additionally, the authors searched for eight function modules that were subsequently applied to each network layer. Expanding upon these concepts, SNAS-GCN represents an extension of this research, optimizing the search space and implementing a single-path one-shot approach for improved search efficiency [22]. The experimental findings of SNAS-GCN demonstrate a reduction in search time compared to the previous work [21], albeit with a diminished level of accuracy.…”

“…To date, NAS research has primarily focused on and applied to image classification tasks. In the domain of HAR GCNs, the NAS research focus has been limited to [21] and [22], to the best of our knowledge. In the work of [21], NAS was employed to discover the most effective architecture by approximating spatiotemporal cues and leveraging Chebyshev polynomials of varying orders.…”

“…The experimental findings of SNAS-GCN demonstrate a reduction in search time compared to the previous work [21], albeit with a diminished level of accuracy. Despite the significant contributions of [21] and [22] to the field of skeleton GCNs through utilizing NAS, their work has certain limitations. One limitation lies in their reliance on approximating spatiotemporal cues and utilizing Chebyshev polynomials, which can lead to information loss and impact the derived architectures' accuracy and robustness.…”

Multi-Scale Spatial–Spectral Attention-Based Neural Architecture Search for Hyperspectral Image Classification

“…The need to enhance the efficiency of automatic neural network design technology is motivated by both the expense of computing resources and the burden of parameter adjustment [22]. NAS has been applied to many tasks and has achieved remarkable results, such as speech recognition [23], computer vision [24], and so on.…”