text: 128,192,1334872698133106691,2445322540,Pascal Fua,"We propose a way to exploit contrastive self-supervised learning to extract rich latent vectors from videos. Given this representation, a mapping to 3D pose can be learned from a very little annotated data. <LINK> @HelgeRhodin #DeepLearning #computervision <LINK>",https://arxiv.org/abs/2012.01511,"In this paper we propose an unsupervised learning method to extract temporal information on monocular videos, where we detect and encode subject of interest in each frame and leverage contrastive self-supervised (CSS) learning to extract rich latent vectors. Instead of simply treating the latent features of nearby frames as positive pairs and those of temporally-distant ones as negative pairs as in other CSS approaches, we explicitly disentangle each latent vector into a time-variant component and a time-invariant one. We then show that applying CSS only to the time-variant features and encouraging a gradual transition on them between nearby and away frames while also reconstructing the input, extract rich temporal features into the time-variant component, well-suited for human pose estimation. Our approach reduces error by about 50\% compared to the standard CSS strategies, outperforms other unsupervised single-view methods and matches the performance of multi-view techniques. ","Unsupervised Temporal Learning on Monocular Videos for 3D Human Pose