Multi-Scale Feature Fusion
Overview
Multi-scale feature fusion studies how to combine coarse contextual information with fine spatial, temporal, or spectral details. Common approaches include feature pyramids and skip connections, attention or gated fusion, cross-scale alignment, and adaptive routing across CNN, Transformer, and state-space models. These methods are used in settings such as small-object detection, medical and remote-sensing segmentation, time-series forecasting, and multimodal perception. A central challenge is preserving useful detail while aligning scales and avoiding redundant computation, with results often depending on the task, data, and model size.
Latest papers
187 papers14 papers in the last four weeks, down 30% on the four weeks before. 0.1% of all new papers.