Segmental Attention

Segmental attention is a technique in machine learning that refines attention mechanisms by focusing on specific segments or regions within input data, such as images or video frames, rather than processing the entire input globally. Current research emphasizes improving efficiency and accuracy of segmental attention, particularly within transformer-based architectures and convolutional neural networks, often incorporating techniques like windowed attention and co-attention to handle high-resolution inputs and complex relationships between segments. This approach enhances performance in various applications, including visual question answering, video object segmentation, and action recognition, by enabling more precise feature extraction and improved contextual understanding.