Attention Module
Attention modules are mechanisms within neural networks designed to selectively focus on the most relevant information, improving efficiency and accuracy. Current research emphasizes developing more efficient attention mechanisms, particularly for long sequences and high-dimensional data, exploring variations like selective attention, frequency-aware attention, and low-rank approximations within architectures such as transformers and state-space models. These advancements are significantly impacting various fields, including computer vision (e.g., image and video analysis), natural language processing (e.g., large language models), and healthcare (e.g., medical image analysis), by enhancing model performance and reducing computational costs.
Papers
IDAN: Image Difference Attention Network for Change Detection
Hongkun Liu, Zican Hu, Qichen Ding, Xueyun Chen
KAM -- a Kernel Attention Module for Emotion Classification with EEG Data
Dongyang Kuang, Craig Michoski
A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data
Dongyang Kuang, Craig Michoski, Wenting Li, Rui Guo