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Found 4 papers, 1 papers with code
Exploiting Global and Local Attentions for Heavy Rain Removal on Single Images
In this work, we propose a novel network architecture consisting of three sub-networks to remove heavy rain from a single image without estimating rain streaks and fog separately.
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes
However, previous methods usually learn forward or backward image synthesis, but not depth estimation, as they cannot effectively neglect occlusions between the target and the reference images.
A HVS-inspired Attention to Improve Loss Metrics for CNN-based Perception-Oriented Super-Resolution
We identify regions in input images, based on the underlying spatial frequency, which are not generally well reconstructed during Super-Resolution but are most important in terms of visual sensitivity.
Finding Correspondences for Optical Flow and Disparity Estimations using a Sub-pixel Convolution-based Encoder-Decoder Network
We show a supervised end-to-end training of our proposed networks for optical flow and disparity estimations, and an unsupervised end-to-end training for monocular depth and pose estimations.
