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.
1 code implementation • 22 Dec 2023 • Anish Madan, Neehar Peri, Shu Kong, Deva Ramanan
In this work, we propose Foundational FSOD, a new benchmark protocol that evaluates detectors pre-trained on any external datasets and fine-tuned on K-shots per target class.
1 code implementation • 1 Jan 2021 • Anish Madan, Ranjitha Prasad
We demonstrate the performance of B-MAML using classification and regression tasks, and highlight that training a sparsifying BNN using MAML indeed improves the parameter footprint of the model while performing at par or even outperforming the MAML approach.
1 code implementation • 18 Jun 2020 • Lokender Tiwari, Anish Madan, Saket Anand, Subhashis Banerjee
Specifically, we devise an ensemble of these generative classifiers that rank-aggregates their predictions via a Borda count-based consensus.
1 code implementation • 15 May 2020 • Shagun Uppal, Anish Madan, Sarthak Bhagat, Yi Yu, Rajiv Ratn Shah
In this paper, we try to exploit the different visual cues and concepts in an image to generate questions using a variational autoencoder (VAE) without ground-truth answers.