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Total Variation Optimization Layers for Computer Vision
- Source :
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
- Publication Year :
- 2022
- Publisher :
- IEEE, 2022.
-
Abstract
- Optimization within a layer of a deep-net has emerged as a new direction for deep-net layer design. However, there are two main challenges when applying these layers to computer vision tasks: (a) which optimization problem within a layer is useful?; (b) how to ensure that computation within a layer remains efficient? To study question (a), in this work, we propose total variation (TV) minimization as a layer for computer vision. Motivated by the success of total variation in image processing, we hypothesize that TV as a layer provides useful inductive bias for deep-nets too. We study this hypothesis on five computer vision tasks: image classification, weakly supervised object localization, edge-preserving smoothing, edge detection, and image denoising, improving over existing baselines. To achieve these results we had to address question (b): we developed a GPU-based projected-Newton method which is $37\times$ faster than existing solutions.<br />CVPR 2022
Details
- Database :
- OpenAIRE
- Journal :
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Accession number :
- edsair.doi.dedup.....76fcc23e5cd45fabb3b5b0a53d131246