no code implementations • 9 May 2024 • Yash Khandelwal, Mayur Arvind, Sriram Kumar, Ashish Gupta, Sachin Kumar Danisetty, Piyush Bagad, Anish Madan, Mayank Lunayach, Aditya Annavajjala, Abhishek Maiti, Sansiddh Jain, Aman Dalmia, Namrata Deka, Jerome White, Jigar Doshi, Angjoo Kanazawa, Rahul Panicker, Alpan Raval, Srinivas Rana, Makarand Tapaswi
Our goal is to equip health workers and public health systems with a solution for contactless newborn anthropometry in the community.
no code implementations • 19 Oct 2023 • Mayank Lunayach, Sergey Zakharov, Dian Chen, Rares Ambrus, Zsolt Kira, Muhammad Zubair Irshad
In this work, we address the challenging task of 3D object recognition without the reliance on real-world 3D labeled data.
no code implementations • 16 Jun 2022 • Mayank Lunayach, James Smith, Zsolt Kira
Online few-shot learning describes a setting where models are trained and evaluated on a stream of data while learning emerging classes.
no code implementations • 23 Jan 2020 • Badri N. Patro, Mayank Lunayach, Vinay P. Namboodiri
These have two-fold benefits: a) improvement in obtaining the certainty estimates that correlate better with misclassified samples and b) improved attention maps that provide state-of-the-art results in terms of correlation with human attention regions.
no code implementations • ICCV 2019 • Badri N. Patro, Mayank Lunayach, Shivansh Patel, Vinay P. Namboodiri
These have two-fold benefits: a) improvement in obtaining the certainty estimates that correlate better with misclassified samples and b) improved attention maps that provide state-of-the-art results in terms of correlation with human attention regions.