Dynamic Mask

Dynamic masking techniques are revolutionizing various computer vision tasks by selectively focusing on or excluding parts of input data during model training and inference. Current research emphasizes developing sophisticated masking strategies, including geometrically informed, asymmetric, and multiple dynamic masks, often integrated within masked autoencoders, transformers, or diffusion models, to improve efficiency and accuracy. These advancements are significantly impacting fields like image and point cloud processing, leading to improved performance in image inpainting, denoising, segmentation, and model compression, as well as enhancing the explainability and robustness of deep learning models.