Residual Channel Attention

Residual channel attention mechanisms are being extensively explored to improve the performance of deep learning models across various computer vision tasks, including image reconstruction, object detection, and video anomaly detection. Research focuses on integrating these mechanisms into existing architectures like autoencoders and Swin Transformers, often employing them alongside other techniques such as multi-scale processing and memory modules to enhance feature extraction and representation learning. This refined feature selection improves model accuracy and efficiency by selectively emphasizing relevant information within feature maps, leading to significant advancements in image and video processing applications.