|FIELD||AI and Natural Sciences|
|DATE||July 28 (Wed), 2021|
|TITLE||Contrastive Learning for Pixel-level Representation and Correspondence|
Numerous computer vision and computational photography applications require the points on an object in one image to be matched with their corresponding object points in another image. Establishing such pixel-level correspondences can facilitate a variety of computer vision applications including nonparametric scene parsing, semantic segmentation, object detection, and image manipulation. A major challenge of correspondence learning with recent deep CNNs is the lack of ground-truth correspondence maps for training. More recently, contrastive learning has been an effective tool in unsupervised visual representation learning, but there have been no studies to learn pixel-level correspondences with the contrastive learning. To move step forward, we have explored how the dense correspondence benefits from the contrastive learning paradigm. This talk summarizes our main efforts to incorporate contrastive learning for establishing pixel-level correspondences and its applications including semantic correspondence, landmark detection, photorealistic style transfer, and exemplar-based image-to-image translation.