DSPL: Dual-Space Prompt Learning With Context Bias Decoupling for Open Set Recognition

Open Set Recognition (OSR) aims to accurately classify known classes and effectively reject unknown classes in open-world scenarios, which is essential for building safe and reliable intelligent systems. Existing studies have shown that auxiliary data-based methods—utilizing outlier exposure or prompt-based guidance to strengthen OSR models—have achieved significant improvements. However, these methods are highly sensitive to the selection of data. Considering the demand for domain expertise and the cost implications, synthesizing data from known samples becomes a preferable alternative. To achieve performance on par with methods that incorporate auxiliary data, it is essential to address the following key challenges: (1) obtaining meaningful pseudo-unknown samples and prompts without manual intervention, and (2) establishing a reliable recognition mechanism during the inference phase. In this paper, we propose a novel Dual-Space Prompt Learning with Context Bias Decoupling (DSPL) method to address the above issues. Specifically, we symmetrically model the same number of unknown classes as known classes to achieve a balanced class distribution in the embedding space. Learnable textual templates are designed for both types of data. Meanwhile, boundary samples are synthesized and treated as pseudo-unknown samples by decoupling discriminative and co-occurring features. Additionally, unknown sample detection is performed by integrating the maximum posterior over known and unknown classes, thereby extending the advantages of symmetric modeling from the training phase to inference. Extensive experiments demonstrate that the proposed method DSPL achieves state-of-the-art performance compared with the most advanced OSR methods.