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Conference Paper
Task-Adaptive Open-Set Detection with Prompt-Tuned Adaptors
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- Authors
- Kimin Yun, Kangmin Bae, Yuseok Bae
- Issue Date
- 2025-08
- Citation
- International Conference on Advanced Video and Signal-based Surveillance (AVSS) 2025, pp.1-6
- Language
- English
- Type
- Conference Paper
- Abstract
- 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.
- KSP Keywords
- Closed-set, Conditional inference, Detection Framework, Fine-tuning, Open-set, Person detection, Task-specific, Zero-shot, inference mechanism