Coarse to Fine Alignment

Coarse-to-fine alignment is a computational approach that refines initial, broad alignments between different data modalities (e.g., text and video, speech and image, or 3D point clouds) with increasingly precise matching at finer levels of detail. Current research focuses on developing efficient algorithms, often incorporating contrastive learning and optimization techniques like factor graph optimization or alternating minimization, to achieve robust and accurate alignments across diverse data types. This methodology improves the performance of various applications, including text-video retrieval, speech-image retrieval, and non-rigid registration, by effectively bridging semantic gaps and leveraging both global context and local features. The resulting improvements in accuracy and efficiency have significant implications for various fields, ranging from computer vision and natural language processing to robotics and autonomous navigation.