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Piyush Tiwary

5 accepted papers

2026

MAgSeg: Segmentation of Agricultural Landscapes in High-Resolution Satellite Imagery using Multimodal Large Language Models

Piyush Tiwary, Utkarsh Ahuja, Depanshu Sani, Aishwarya Jayagopal, Sagar Gubbi, Subhashini Venugopalan +2
IJCAI 2026

Agricultural landscape segmentation in the Global South is challenging as it is characterized by fragmented plots, high intra-class variance, and a scarcity of labeled training data. Recent advances in segmentation have been made by Multimodal Large Language Models (MLLMs). However, current approach

Cited by 0Scholar ↗
2025

LangDAug: Langevin Data Augmentation for Multi-Source Domain Generalization in Medical Image Segmentation

Piyush Tiwary, Kinjawl Bhattacharyya, Prathosh AP
ICML 2025poster

Medical image segmentation models often struggle to generalize across different domains due to various reasons. Domain Generalization (DG) methods overcome this either through representation learning or data augmentation (DA). While representation learning methods seek domain-invariant features, the…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2024

Bayesian Pseudo-Coresets via Contrastive Divergence

Piyush Tiwary, Kumar Shubham, Vivek V. Kashyap, Prathosh A. P.
UAI 2024poster

Bayesian methods provide an elegant framework for estimating parameter posteriors and quantification of uncertainty associated with probabilistic models. However, they often suffer from slow inference times. To address this challenge, Bayesian Pseudo-Coresets (BPC) have emerged as a promising soluti…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2023

Few-shot Cross-domain Image Generation via Inference-time Latent-code Learning

Arnab Kumar Mondal, Piyush Tiwary, Parag Singla, Prathosh AP
ICLR 2023top-25%

In this work, our objective is to adapt a Deep generative model trained on a large-scale source dataset to multiple target domains with scarce data. Specifically, we focus on adapting a pre-trained Generative Adversarial Network (GAN) to a target domain without re-training the generator. Our method…

Cited by 15Source ↗PDF ↗Scholar ↗
2023

Minority Oversampling for Imbalanced Data via Class-Preserving Regularized Auto-Encoders

Arnab Kumar Mondal, Lakshya Singhal, Piyush Tiwary, Parag Singla, Prathosh AP
AISTATS 2023poster

Class imbalance is a common phenomenon in multiple application domains such as healthcare, where the sample occurrence of one or few class categories is more prevalent in the dataset than the rest. This work addresses the class-imbalance issue by proposing an over-sampling method for the minority cl…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗