AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition

TL;DR

AdaSGN introduces an adaptive method for skeleton-based action recognition that dynamically adjusts joint input size and model complexity per sample, significantly improving efficiency while maintaining or enhancing accuracy.

Contribution

The paper proposes AdaSGN, a novel approach that adaptively controls joint input size and model complexity for more efficient skeleton-based action recognition.

Findings

Achieves comparable or higher accuracy than baselines.

Reduces GFLOPs significantly across datasets.

Demonstrates effectiveness on NTU-60, NTU-120, and SHREC.

Abstract

Existing methods for skeleton-based action recognition mainly focus on improving the recognition accuracy, whereas the efficiency of the model is rarely considered. Recently, there are some works trying to speed up the skeleton modeling by designing light-weight modules. However, in addition to the model size, the amount of the data involved in the calculation is also an important factor for the running speed, especially for the skeleton data where most of the joints are redundant or non-informative to identify a specific skeleton. Besides, previous works usually employ one fix-sized model for all the samples regardless of the difficulty of recognition, which wastes computations for easy samples. To address these limitations, a novel approach, called AdaSGN, is proposed in this paper, which can reduce the computational cost of the inference process by adaptively controlling the input number of the joints of the skeleton on-the-fly. Moreover, it can also adaptively select the optimal model size for each sample to achieve a better trade-off between accuracy and efficiency. We conduct extensive experiments on three challenging datasets, namely, NTU-60, NTU-120 and SHREC, to verify the superiority of the proposed approach, where AdaSGN achieves comparable or even higher performance with much lower GFLOPs compared with the baseline method.