81 citations · 275 across the 51 of their papers we have counts for
75 papers
NumBench: Diagnosing Counting Failures in Text-to-Image Models
Sandeep Wadhwa, Mayank Vatsa, Richa Singh +2
Text-to-image (T2I) models often generate the wrong number of objects, yet existing benchmarks are too small or weakly controlled to explain why. We introduce \textbf{NumBench}, a…
Latent Performance Profiling of Large Language Models
Tanmoy Chakraborty, Ayan Sengupta, Suparna Bhattacharya +7
Large language models (LLMs) frequently achieve impressive scores on standardized benchmarks, yet accuracy alone offers a limited view of their capabilities. Evaluating open-source…
Unbiased Model Prediction Without Using Protected Attribute Information
Puspita Majumdar, Surbhi Mittal, Saheb Chhabra +2
The problem of bias persists in the deep learning community as models continue to provide disparate performance across different demographic subgroups. Therefore, several algorithm…
NutriScreener: Retrieval-Augmented Multi-Pose Graph Attention Network for Malnourishment Screening
Misaal Khan, Mayank Vatsa, Kuldeep Singh +1
Child malnutrition remains a global crisis, yet existing screening methods are laborious and poorly scalable, hindering early intervention. In this work, we present NutriScreener,…
Right Looks, Wrong Reasons: Compositional Fidelity in Text-to-Image Generation
Mayank Vatsa, Aparna Bharati, Richa Singh
The architectural blueprint of today's leading text-to-image models contains a fundamental flaw: an inability to handle logical composition. This survey investigates this breakdown…
TAIGen: Training-Free Adversarial Image Generation via Diffusion Models
Susim Roy, Anubhooti Jain, Mayank Vatsa +1
Adversarial attacks from generative models often produce low-quality images and require substantial computational resources. Diffusion models, though capable of high-quality genera…