Progressive Instance-Aware Feature Learning for Compositional Action Recognition

In order to enable the model to generalize to unseen “action-objects” (compositional action), previous methods encode multiple pieces of information (i.e., the appearance, position, and identity of visual instances) independently and concatenate them for classification. However, these methods ignore the potential supervisory role of instance information (i.e., position and identity) in the process of visual perception. To this end, we present a novel framework, namely Progressive Instance-aware Feature Learning (PIFL), to progressively extract, reason, and predict dynamic cues of moving instances from videos for compositional action recognition. Specifically, this framework extracts features from foreground instances that are likely to be relevant to human actions (Position-aware Appearance Feature Extraction in Section III-B1), performs identity-aware reasoning among instance-centric features with semantic-specific interactions (Identity-aware Feature Interaction in Section III-B2), and finally predicts instances’ position from observed states to force the model into perceiving their movement (Semantic-aware Position Prediction in Section III-B3). We evaluate our approach on two compositional action recognition benchmarks, namely, Something-Else and IKEA-Assembly. Our approach achieves consistent accuracy gain beyond off-the-shelf action recognition algorithms in terms of both ground truth and detected position of instances.