Ensuring the reliability of open-world intelligent systems heavily relies on effective out-of-distribution (OOD) detection. Despite notable successes in existing OOD detection methods, their performance in scenarios with limited training samples is still suboptimal. Therefore, we first construct a comprehensive few-shot OOD detection benchmark in this paper. Remarkably, our investigation reveals that Parameter-Efficient Fine-Tuning (PEFT) techniques, such as visual prompt tuning and visual adapter tuning, outperform traditional methods like fully fine-tuning and linear probing tuning in few-shot OOD detection. Considering that some valuable information from the pre-trained model, which is conducive to OOD detection, may be lost during the fine-tuning process, we reutilize features from the pre-trained models to mitigate this issue. Specifically, we first propose a training-free approach, termed uncertainty score ensemble (USE). This method integrates feature-matching scores to enhance existing OOD detection methods, significantly narrowing the gap between traditional fine-tuning and PEFT techniques. However, due to its training-free property, this method is unable to improve in-distribution accuracy. To this end, we further propose a method called Domain-Specific and General Knowledge Fusion (DSGF) to improve few-shot OOD detection performance and ID accuracy under different fine-tuning paradigms. Experiment results demonstrate that DSGF enhances few-shot OOD detection across different fine-tuning strategies, shot settings, and OOD detection methods. We believe our work can provide the research community with a novel path to leveraging large-scale visual pre-trained models for addressing FS-OOD detection. The code will be released.

Continual Generalized Category Discovery (C-GCD) is an emerging research direction in Open-World Learning. The model aims to incrementally discover novel classes from unlabeled data while maintaining recognition of previously learned classes, without accessing historical samples. The absence of supervision signal in incremental sessions makes catastrophic forgetting more severe than in traditional incremental learning. Existing methods primarily enhance generalization through single-modality contrastive learning, overlooking the natural advantages of textual information. Visual features capture perceptual details such as shapes and textures, while textual information helps distinguish visually similar but semantically distinct categories, offering complementary benefits. However, directly obtaining category descriptions for unlabeled data in C-GCD is challenging. To address this, we introduce a conditional prompt learning mechanism to generate pseudo-prompts as textual information for unlabeled samples. Additionally, we propose a dual-modal contrastive learning strategy to enhance vision-text alignment and exploit CLIP’s multimodal potential. Extensive experiments on four benchmark datasets demonstrate that our method achieves competitive performance. We hope this work provides new insights for future research.