Task Adaptive Modeling for Few-shot Action Recognition

Collecting action recognition datasets is time-consuming and labor-intensive. To solve this problem, a few-shot action recognition task that uses episode training to learn the model appears. However, due to the randomness of few-shot learning task sampling, there are great differences between each task, and the characteristics of classes are also diverse. Most of the current methods simply use the same processing flow for action recognition, ignoring the correlation between tasks. To solve this problem, we propose a task adaptive network for few-shot action recognition, which utilizes the dependency of support set and query set categories. Our method mainly includes two key points: Firstly, we add an attention module after the feature extraction module, which can use the attention mechanism to focus the obtained feature representation on more important local information. Secondly, we design a task adaptive module, which uses the support set samples to strengthen all samples of the current task. The module strengthens the common features within each class of the support set and expands the query set to highlight the differences between classes. We have conducted a large number of experiments on two commonly used action recognition data sets: HMDB51 and UCF101. The results of experiments show that our method has strong competitiveness and performs well in the field of few-shot action recognition.