Full Perception Head: Bridging the Gap Between Local and Global Features

Abstract

Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image. Local features extracted by convolutions, etc., capture fine-grained details such as edges and textures, while global features extracted by full connection layers, etc., represent the overall structure and long-range relationships within the image. These features are crucial for accurate object detection, yet most existing methods focus on aggregating local and global features, often overlooking the importance of medium-range dependencies. To address this gap, we propose a novel full perception module (FP-Module), a simple yet effective feature extraction module designed to simultaneously capture local details, medium-range dependencies, and long-range dependencies. Building on this, we construct a full perception head (FP-Head) by cascading multiple FP-Modules, enabling the prediction layer to leverage the most informative features. Experimental results in the MS COCO dataset demonstrate that our approach significantly enhances object recognition and localization, achieving 2.7-5.7 APval gains when integrated into standard object detectors. Notably, the FP-Module is a universal solution that can be seamlessly incorporated into existing detectors to boost performance. The code will be released at https://github.com/Idcogroup/FP-Head.