1 code implementation • 27 Feb 2023 • Prashant Pandey, Mustafa Chasmai, Monish Natarajan, Brejesh lall
To this end, we propose a novel unified weakly supervised OVSS pipeline that can perform ZSS, FSS and Cross-dataset segmentation on novel classes without using pixel-level labels for either the base (seen) or the novel (unseen) classes in an inductive setting.
1 code implementation • 7 Oct 2022 • Prashant Pandey, Aleti Vardhan, Mustafa Chasmai, Tanuj Sur, Brejesh lall
We show that our framework is more robust compared to traditional adversarial defense mechanisms such as adversarial training.
1 code implementation • 26 Aug 2022 • Prashant Pandey, Mustafa Chasmai, Tanuj Sur, Brejesh lall
We further demonstrate that while many existing Deep CNN based methods tend to be extremely vulnerable to adversarial attacks, R-PNODE exhibits increased adversarial robustness for a wide array of these attacks.
no code implementations • CVPR 2021 • Prashant Pandey, Mrigank Raman, Sumanth Varambally, Prathosh AP
Generalization of machine learning models trained on a set of source domains on unseen target domains with different statistics, is a challenging problem.
no code implementations • 12 Jun 2021 • Prashant Pandey, Ajey Pai, Nisarg Bhatt, Prasenjit Das, Govind Makharia, Prathosh AP, Mausam
We evaluate our method on four public medical segmentation datasets and a novel histopathology dataset that we introduce.
no code implementations • 1 Mar 2021 • Prashant Pandey, Mrigank Raman, Sumanth Varambally, Prathosh AP
Generalization of machine learning models trained on a set of source domains on unseen target domains with different statistics, is a challenging problem.
no code implementations • 28 Jul 2020 • Prashant Pandey, Mrigank Raman, Sumanth Varambally, Prathosh AP
Features extracted from this source domain are learned using a generative model whose latent space is used as a sampler to retrieve the nearest neighbors for the target data points.
2 code implementations • ECCV 2020 • Prashant Pandey, Aayush Kumar Tyagi, Sameer Ambekar, Prathosh AP
Therefore we cast the skin segmentation problem as that of target-independent Unsupervised Domain Adaptation (UDA) where we use the data from the Red-channel of the visible-range to develop skin segmentation algorithm on NIR images.
1 code implementation • 11 May 2020 • Prashant Pandey, Prathosh AP, Vinay Kyatham, Deepak Mishra, Tathagato Rai Dastidar
We prove the existence of such a clone given that infinite number of data points can be sampled from the source distribution.
1 code implementation • 11 Nov 2019 • Prashant Pandey, Prathosh AP, Manu Kohli, Josh Pritchard
In this paper, we propose to automate the response measurement through video recording of the scene following the use of Deep Neural models for human action recognition from videos.
no code implementations • 27 Sep 2018 • Deepak Mishra, Prathosh AP, Aravind J, Prashant Pandey, Santanu Chaudhury
Conditional generation refers to the process of sampling from an unknown distribution conditioned on semantics of the data.