Proposal-level Correction Guided by CLIP for Few-shot Object Detection
Ruihang Wang, Taijin Zhao, Hefei Mei, Heqian Qiu, Lanxiao Wang, Hongliang Li · 2024
Few-shot object detection aims at detecting previously unseen objects given only a few annotated samples. Most existing approaches treat the model obtained from the base training stage with abundant data as a container of prior knowledge that can be transferred to novel objects. Knowledge with similar properties is also contained in Contrastive Language-Image Pretraining (CLIP). In this paper, we utilize this external prior knowledge to generate proposal-level classification scores to improve the detection results. We notice that these scores can hardly reflect the quality of proposal localization, so we combine them with the ones from a conventional detector to obtain the ability to distinguish the background. Moreover, we propose a new score fusion module with regularization to alleviate the ambiguity of detection results generated by a trivial element-wise multiplication fusion method. To further improve the quality of classification scores in our proposed branch, we add learnable prompts to mitigate the inaccurate classification problem we observe. We conduct extensive experiments on the PASCAL VOC dataset and demonstrate the effectiveness of our approach.