Adaptive Prototype Model for Attribute-based Multi-label Few-shot Action Recognition
This work addresses the challenge of comprehensive human behavior understanding in action recognition systems, though it appears incremental as it builds on existing few-shot and multi-label approaches.
The paper tackles the problem of decreased accuracy when using a single model for multi-attribute action recognition by proposing the Adaptive Attribute Prototype Model (AAPM), which achieves state-of-the-art performance in attribute-based multi-label and single-label few-shot action recognition.
In real-world action recognition systems, incorporating more attributes helps achieve a more comprehensive understanding of human behavior. However, using a single model to simultaneously recognize multiple attributes can lead to a decrease in accuracy. In this work, we propose a novel method i.e. Adaptive Attribute Prototype Model (AAPM) for human action recognition, which captures rich action-relevant attribute information and strikes a balance between accuracy and robustness. Firstly, we introduce the Text-Constrain Module (TCM) to incorporate textual information from potential labels, and constrain the construction of different attributes prototype representations. In addition, we explore the Attribute Assignment Method (AAM) to address the issue of training bias and increase robustness during the training process.Furthermore, we construct a new video dataset with attribute-based multi-label called Multi-Kinetics for evaluation, which contains various attribute labels (e.g. action, scene, object, etc.) related to human behavior. Extensive experiments demonstrate that our AAPM achieves the state-of-the-art performance in both attribute-based multi-label few-shot action recognition and single-label few-shot action recognition. The project and dataset are available at an anonymous account https://github.com/theAAPM/AAPM