1 code implementation • 5 Sep 2022 • Abhay Rawat, Isha Dua, Saurav Gupta, Rahul Tallamraju
In our approach, to align the two domains, we leverage contrastive losses to learn a semantically meaningful and a domain agnostic feature space using the supervised samples from both domains.
1 code implementation • 27 Aug 2022 • Midhun Vayyat, Jaswin Kasi, Anuraag Bhattacharya, Shuaib Ahmed, Rahul Tallamraju
In this work, we propose CLUDA, a simple, yet novel method for performing unsupervised domain adaptation (UDA) for semantic segmentation by incorporating contrastive losses into a student-teacher learning paradigm, that makes use of pseudo-labels generated from the target domain by the teacher network.
Ranked #1 on Unsupervised Domain Adaptation on GTA5-to-Cityscapes
1 code implementation • 14 Apr 2022 • Saurav Gupta, Sourav Lakhotia, Abhay Rawat, Rahul Tallamraju
Common challenges that image classification models encounter when localizing objects are, (a) they tend to look at the most discriminative features in an image that confines the localization map to a very small region, (b) the localization maps are class agnostic, and the models highlight objects of multiple classes in the same image and, (c) the localization performance is affected by background noise.
no code implementations • 13 Jul 2020 • Rahul Tallamraju, Nitin Saini, Elia Bonetto, Michael Pabst, Yu Tang Liu, Michael J. Black, Aamir Ahmad
We focus on vision-based MoCap, where the objective is to estimate the trajectory of body pose and shape of a single moving person using multiple micro aerial vehicles.