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
Drawing Attention to Detail: Pose Alignment through Self-Attention for Fine-Grained Object Classification
Salwa Al Khatib, Mohamed El Amine Boudjoghra, Jameel Hassan
Weakly Supervised Human Skin Segmentation using Guidance Attention Mechanisms
Kooshan Hashemifard, Pau Climent-Perez, Francisco Florez-Revuelta
MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing
Zhe Li, Zhongwen Rao, Lujia Pan, Zenglin Xu