MOS: Modeling Object-Scene Associations in Generalized Category Discovery
This work improves classification for both base and novel classes in unlabeled images, particularly in fine-grained domains, but is incremental as it builds on existing GCD methods by incorporating scene information.
The paper tackles the problem of Generalized Category Discovery (GCD) by addressing the Ambiguity Challenge, where scene information is misinterpreted, and proposes the MOS framework to leverage scene information as a prior, achieving a 4% average accuracy improvement on fine-grained datasets compared to state-of-the-art methods.
Generalized Category Discovery (GCD) is a classification task that aims to classify both base and novel classes in unlabeled images, using knowledge from a labeled dataset. In GCD, previous research overlooks scene information or treats it as noise, reducing its impact during model training. However, in this paper, we argue that scene information should be viewed as a strong prior for inferring novel classes. We attribute the misinterpretation of scene information to a key factor: the Ambiguity Challenge inherent in GCD. Specifically, novel objects in base scenes might be wrongly classified into base categories, while base objects in novel scenes might be mistakenly recognized as novel categories. Once the ambiguity challenge is addressed, scene information can reach its full potential, significantly enhancing the performance of GCD models. To more effectively leverage scene information, we propose the Modeling Object-Scene Associations (MOS) framework, which utilizes a simple MLP-based scene-awareness module to enhance GCD performance. It achieves an exceptional average accuracy improvement of 4% on the challenging fine-grained datasets compared to state-of-the-art methods, emphasizing its superior performance in fine-grained GCD. The code is publicly available at https://github.com/JethroPeng/MOS