# Multi-Resolution and Semantic-Aware Bidirectional Adapter for Multi-Scale Object Detection
###### Abstract
More precisely, this framework comprises three sequential components: multi-resolution cascaded fusion (MCF), a semantic-aware refinement transformer (SRT), and bidirectional fine-grained interaction (BFI). Respectively, these three components target the input, enhancement, and interaction aspects of the feature integration process through a coarse-to-fine strategy. The MCF component receives inputs in the form of multi-stage features and multi-resolution images from the backbone. It then adaptively extracts suitable multi-level features tailored to distinct object instances through a cascaded fusion strategy involving multiple receptive fields. Additionally, SRT is introduced to enhance the multi-scale semantic representation by refining both detailed and global semantic information while minimizing computational costs. SRT is designed with a semantic association strategy and employs multi-branch attention to effectively integrate semantic information across diverse scales. Moreover, to achieve a versatile and effective feature interaction, we introduce BFI, a mechanism for establishing a bidirectional flow of information. The bottom-up interaction is intended to furnish spatial guidance transitioning from low-level to high-level layers, fostering interaction across multiple levels. By leveraging intricate spatial information from low-level layers, high-level layers can effectively identify salient regions and provide enhanced semantic information with greater accuracy. Conversely, the top-down interaction is employed to establish semantic enhancement from high-level layers to low-level layers. Building upon the copious semantic information in the high-level layers, low-level layers can exhibit a comprehensive comprehension of object instances. In conclusion, the introduced coarse-to-fine process allows for the attainment of a more potent representation of objects across multiple scales. Thorough experiments are carried out to validate the efficacy of the proposed approach. The introduced MSBA serves as a plug-and-play framework that seamlessly integrates with diverse backbones and detectors. On the MS COCO dataset, our method consistently outperforms state-of-the-art methods, achieving superior performance across different backbones and detectors, without any additional bells and whistles. As depicted in Figure 1, our detection results, presented in the second column, demonstrate superiority in accurately detecting multi-scale objects. In summary, this study offers the following key contributions:  
* To mitigate the challenge of scale variation, we introduce a novel multi-resolution and semantic-aware bidirectional adapter for multi-scale object detection, referred to as MSBA. It alleviates the scale-variant issue by addressing the input, refinement, and interaction facets of feature integration. * Our proposition, MSBA, is composed of multi-resolution cascaded fusion (MCF), a semantic-aware refinement transformer (SRT), and bidirectional fine-grained interaction (BFI). SRT is dedicated to refining the multi-scale semantic representation, while BFI is employed to foster ample interaction across various levels. Importantly, all these modules are pluggable. * The proposed method is rigorously evaluated on the widely used MS-COCO dataset, demonstrating its superiority over state-of-the-art approaches. Thorough ablation experiments are conducted to confirm the efficacy of the proposed modules within the MSBA framework.