Compositional Learning in Transformer-Based Human-Object Interaction Detection

• URL: http://arxiv.org/abs/2308.05961v1
• Date: Fri, 11 Aug 2023 06:41:20 GMT
• Title: Compositional Learning in Transformer-Based Human-Object Interaction Detection
• Authors: Zikun Zhuang, Ruihao Qian, Chi Xie, Shuang Liang
• Abstract summary: Long-tailed distribution of labeled instances is a primary challenge in HOI detection. Inspired by the nature of HOI triplets, some existing approaches adopt the idea of compositional learning. We creatively propose a transformer-based framework for compositional HOI learning.
• Score: 6.630793383852106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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