Cross-Layer Feature Self-Attention Module for Multi-Scale Object Detection

• URL: http://arxiv.org/abs/2510.14726v1
• Date: Thu, 16 Oct 2025 14:25:21 GMT
• Title: Cross-Layer Feature Self-Attention Module for Multi-Scale Object Detection
• Authors: Dingzhou Xie, Rushi Lan, Cheng Pang, Enhao Ning, Jiahao Zeng, Wei Zheng,
• Abstract summary: Cross-Layer Feature Self-Attention Module (CFSAM)<n>CFSAM holistically models both local and global dependencies within multi-scale feature maps.<n>When integrated into the SSD300 framework, CFSAM significantly boosts detection performance.
• Score: 14.148258708632211
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent object detection methods have made remarkable progress by leveraging attention mechanisms to improve feature discriminability. However, most existing approaches are confined to refining single-layer or fusing dual-layer features, overlooking the rich inter-layer dependencies across multi-scale representations. This limits their ability to capture comprehensive contextual information essential for detecting objects with large scale variations. In this paper, we propose a novel Cross-Layer Feature Self-Attention Module (CFSAM), which holistically models both local and global dependencies within multi-scale feature maps. CFSAM consists of three key components: a convolutional local feature extractor, a Transformer-based global modeling unit that efficiently captures cross-layer interactions, and a feature fusion mechanism to restore and enhance the original representations. When integrated into the SSD300 framework, CFSAM significantly boosts detection performance, achieving 78.6% mAP on PASCAL VOC (vs. 75.5% baseline) and 52.1% mAP on COCO (vs. 43.1% baseline), outperforming existing attention modules. Moreover, the module accelerates convergence during training without introducing substantial computational overhead. Our work highlights the importance of explicit cross-layer attention modeling in advancing multi-scale object detection.

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