Cross-Attention
Overview
Cross-attention studies how queries from one representation can select and combine relevant information from another, supporting tasks such as image-text understanding, sensor fusion, and sequence forecasting. Common approaches include bidirectional or gated fusion, multi-scale attention, and sparse or geometry-constrained routing, with applications ranging from vision-language models and diffusion generation to medical imaging and robotics. Evaluation spans benchmarks for image recognition, segmentation, multimodal question answering, forecasting, and cross-view matching, with growing emphasis on efficiency and robustness to missing or unreliable inputs. A central tension is that selective attention can improve fusion and control, but attention maps are not always faithful explanations, and simpler concatenation can work better when features are already aligned.
Latest papers
78 papers9 papers in the last four weeks, up 13% on the four weeks before. 0.1% of all new papers.