  Human-object interaction (HOI) detection is an important part of
understanding human activities and visual scenes. The long-tailed distribution
of labeled instances is a primary challenge in HOI detection, promoting
research in few-shot and zero-shot learning. Inspired by the combinatorial
nature of HOI triplets, some existing approaches adopt the idea of
compositional learning, in which object and action features are learned
individually and re-composed as new training samples. However, these methods
follow the CNN-based two-stage paradigm with limited feature extraction
ability, and often rely on auxiliary information for better performance.
Without introducing any additional information, we creatively propose a
transformer-based framework for compositional HOI learning. Human-object pair
representations and interaction representations are re-composed across
different HOI instances, which involves richer contextual information and
promotes the generalization of knowledge. Experiments show our simple but
effective method achieves state-of-the-art performance, especially on rare HOI
classes.
