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The Hybrid Convolution and Attention Network (HCANet) introduced a Convolution and Attention Fusion Module (CAFM) that jointly models local features via convolutions and global context via self‑attention. This design is particular beneficial for detecting small objects, where fine spatial details and long‑range dependencies both matter. In this ...

Hybrid Attention Transformer (HAT) Paper HAT: Hybrid Attention Transformer for Image Restoration Architecture Overview The HAT model consists of three main components: shallow feature extraction, deep feature extraction, and image reconstruction. Algorithm Principle The HAT approach integrates channel attention and window-based self-attention m ...

Attention mechanisms have significantly improved the performance of deep learning models in computer vision tasks. This article provides an overview of several popular attention modules that can be easily integrated into object detection models like YOLOv5, YOLOv7, YOLOv8, YOLOv9, and YOLOv10. SE Paper: Squeeze-and-Excitation Networks Link: arX ...

Table of Contents Article Overview Framework Workflow Code Analysis Article Overview Problem Context: Hyperspectral compressive imaging utilizes compressed sensing theory to capture hyperspectral data through snapshot measurements via coded apertures, avoiding temporal scanning. The core challenge lies in reconstructing the original hypers ...