Search Results for author: Prashant Pandey

Found 11 papers, 6 papers with code

A Language-Guided Benchmark for Weakly Supervised Open Vocabulary Semantic Segmentation

1 code implementation27 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.

Few-Shot Semantic Segmentation Language Modelling +4

Adversarially Robust Prototypical Few-shot Segmentation with Neural-ODEs

1 code implementation7 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.

Adversarial Defense Few-Shot Learning +2

Robust Prototypical Few-Shot Organ Segmentation with Regularized Neural-ODEs

1 code implementation26 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.

Adversarial Robustness Few-Shot Learning +3

Generalization on Unseen Domains via Inference-Time Label-Preserving Target Projections

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.

Domain Generalization

Domain Generalization via Inference-time Label-Preserving Target Projections

no code implementations1 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.

Domain Generalization

Discrepancy Minimization in Domain Generalization with Generative Nearest Neighbors

no code implementations28 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.

Domain Generalization

Unsupervised Domain Adaptation for Semantic Segmentation of NIR Images through Generative Latent Search

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.

Segmentation Semantic Segmentation +1

Target-Independent Domain Adaptation for WBC Classification using Generative Latent Search

1 code implementation11 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.

Guided Weak Supervision for Action Recognition with Scarce Data to Assess Skills of Children with Autism

1 code implementation11 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.

Action Recognition

Unsupervised Conditional Generation using noise engineered mode matching GAN

no code implementations27 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.

Attribute Generative Adversarial Network

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