![]() For instance, BYOL with the additional depthĬhannel leads to an increase in downstream classification accuracy from 85.3\% To incorporating depth signals improve the robustness and generalization of theīaseline SSL methods, though the first approach (with depth-channelĬoncatenation) is superior. ImageNet), ImageNet-100 and ImageNet-1k datasets. Methods - BYOL, SimSiam, and SwAV - using ImageNette (10 class subset of We evaluate these two approaches on three different SSL Slightly different camera positions, thereby producing a 3D augmentation forĬontrastive learning. Second, we use the depth signal to generate novel views from ![]() \emph, Ranftl et al., 2021), we explore twoĭistinct approaches to incorporating depth signals into the SSL framework.įirst, we evaluate contrastive learning using an RGB+depth input Provided by a pretrained state-of-the-art monocular RGB-to-depth model (the Low-level biological vision relies heavily on depth cues. Immersive three-dimensional, temporally contiguous environment, and that ![]() TheseĪugmentations ignore the fact that biological vision takes place in an Most SSL methods rely onĪugmentations obtained by transforming the 2D image pixel map. Robust representations useful for downstream tasks. Download a PDF of the paper titled Leveraging the Third Dimension in Contrastive Learning, by Sumukh Aithal and 4 other authors Download PDF Abstract: Self-Supervised Learning (SSL) methods operate on unlabeled data to learn ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |