模式识别国家重点实验室知识库

Scale variation is one of the key challenges of object detection. Multi-level feature fusion is presented to alleviate the problems, e.g., Feature Pyramid Network (FPN) and its extended methods. However, the input features fed into these methods and the interaction among features from different levels are insufficient and rigid. To fully exploit the features of multi-scale objects and enhance the feature interaction, we propose a novel and effective framework called Adaptive Coarse-to-Fine Interactor (ACFI). Specifically, ACFI consists of three cascaded components: Multi-Resolution Fusion (MRF), Fine-Grained Interaction (FGI), and Edge-aware Enhancement (EAE). MRF adaptively extracts multi-level features from multi-resolution images and multi-stage features, and then these features are fed into FGI to have a fine-grained interaction utilizing bottom-up guidance. After that, EAE further refines the features obtained by FGI, and enhances the detailed edge information and suppresses the redundant noise. After the coarse-to-fine process, we can obtain powerful multi-scale representations of various objects. Each component can be embedded into any backbones, separately. Experimental results show the superiority of our method and verify the effectiveness of each proposed module.