Improving small object detection via cross-layer attention.

ABSTRACT: Small object detection is a fundamental and challenging topic in the computer vision community. To detect small objects in images, several methods rely on feature pyramid networks (FPN), which can alleviate the conflict between resolution and semantic information. However, the FPN-based methods also have limitations. First, existing methods only focus only on regions with close spatial distance, hindering the effectiveness of long-range interactions. Second, element-wise addition ignores the different perceptive fields of the two feature layers, thus causing higher-level features to introduce noise to the lower-level features. To address these problems, we propose a cross-layer attention (CLA) block as a generic block for capturing long-range dependencies and reducing noise from high-level

SUBMITTER: Peng R 

PROVIDER: S-EPMC12327818 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature