HODN: Disentangling Human-Object Feature for HOI Detection
This work addresses HOI detection for computer vision applications, offering incremental improvements by explicitly modeling relationships and integrating with existing methods.
The paper tackled the problem of Human-Object Interaction (HOI) detection by addressing ignored relationships among humans, objects, and interactions, proposing HODN with disentangling decoders and mechanisms to improve focus and gradient handling, achieving competitive performance on V-COCO and HICO-Det datasets.
The task of Human-Object Interaction (HOI) detection is to detect humans and their interactions with surrounding objects, where transformer-based methods show dominant advances currently. However, these methods ignore the relationship among humans, objects, and interactions: 1) human features are more contributive than object ones to interaction prediction; 2) interactive information disturbs the detection of objects but helps human detection. In this paper, we propose a Human and Object Disentangling Network (HODN) to model the HOI relationships explicitly, where humans and objects are first detected by two disentangling decoders independently and then processed by an interaction decoder. Considering that human features are more contributive to interaction, we propose a Human-Guide Linking method to make sure the interaction decoder focuses on the human-centric regions with human features as the positional embeddings. To handle the opposite influences of interactions on humans and objects, we propose a Stop-Gradient Mechanism to stop interaction gradients from optimizing the object detection but to allow them to optimize the human detection. Our proposed method achieves competitive performance on both the V-COCO and the HICO-Det datasets. It can be combined with existing methods easily for state-of-the-art results.