Task-Adaptive Open-Set Detection with Prompt-Tuned Adaptors

This paper presents a task-adaptive open-set detection framework that preserves zero-shot performance while incorporating task-specific adaptations for enhanced visual understanding. Our method integrates a frozen zero-shot detector with a learnable, task-specific adaptor module, and employs a token-level conditional inference mechanism using prompt-based feature masking. This approach selectively combines features from the pre-trained zero-shot model and the adapted module within a single forward pass, allowing both general and task-specific representations to contribute effectively. Unlike conventional full fine-tuning that transforms an open-set detector into a closed-set detector, our design maintains the inherent open-set capabilities, thereby mitigating overfitting to task-specific biases. Experimental results on the IHP and VFP290K datasets demonstrate that our method outperforms existing techniques in fallen person detection, underscoring its robustness and practical applicability.