Dense Prediction
Dense prediction, a core task in computer vision, aims to generate a prediction for every pixel in an image, enabling applications like semantic segmentation and depth estimation. Current research focuses on improving the efficiency and accuracy of dense prediction models, exploring architectures like Vision Transformers (ViTs) and convolutional neural networks (CNNs), often combined with techniques such as multi-scale feature fusion, attention mechanisms, and knowledge distillation. These advancements are driving progress in various fields, including medical image analysis, autonomous driving, and remote sensing, by enabling more accurate and efficient processing of high-resolution visual data.
Papers
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