Cross-Modality Prompts: Few-Shot Multi-Label Recognition With Single-Label Training

Few-shot multi-label recognition (FS-MLR) presents a significant challenge due to the need to assign multiple labels to images with limited examples. Existing methods often struggle to balance the learning of novel classes and the retention of knowledge from base classes. To address this issue, we propose a novel Cross-Modality Prompts (CMP) approach. Unlike conventional methods that rely on additional semantic information to mitigate the impact of limited samples, our approach leverages multimodal prompts to adaptively tune the feature extraction network. A new FS-MLR benchmark is also proposed, which includes single-label training and multi-label testing, accompanied by benchmark datasets constructed from MS-COCO and NUS-WIDE. Extensive experiments on these datasets demonstrate the superior performance of our CMP approach, highlighting its effectiveness and adaptability. Our results show that CMP outperforms CoOp on the MS-COCO dataset with a maximal improvement of 19.47% and 23.94% in mAP<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">harmonic</sub> for 5-way 1-shot and 5-way 5-shot settings, respectively.