Affine Transform

Affine transforms, representing geometric transformations like scaling, rotation, and translation, are crucial for image and signal processing, particularly in machine learning applications. Current research focuses on improving the robustness of models to these transformations, employing techniques such as keypoint-based relative position encoding in vision transformers and conditional decoders with temporal-aware affine modules in implicit neural representations for video processing. These advancements enhance the performance of various tasks, including face recognition, video compression, and automated data extraction from images, by enabling more accurate and reliable processing of data subject to geometric distortions.