Make It Easy: Action Quality Assessment of Cyborg Animals Based on Spatial-Temporal Pose Inference

Assessing the action quality of cyborg animals helps to adjust control strategies, guide the development of control algorithm, and enhance the efficiency of navigation and military applications. However, existing research is limited in control algorithm and ignores the assessment of movement smoothness. In this paper, we first propose EASY, an effective assessment framework, which is able to (i) evaluate the action quality of cyborg animals of various sizes and motion patterns; (ii) obtain assessment results directly based on keypoints. Specifically, EASY first captures keypoints sequences through a small object pose estimation module. It employs the convolutional block attention module (CBAM) and depthwise over parameterized convolution (DOConv), allowing more accurate feature recognition with fewer parameters. Then, EASY employs multi-field fusion strategy that merges commonalities and differences, enabling a comprehensive and accurate assessment of fluency. We tested EASY on both CyborgRat-MFA dataset and CyborgBee-MFA dataset. Extensive experiments demonstrate that the assessment results are consistent with human expert scores, which prove that our proposed EASY is reasonable, effective, and interpretable.