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cs.CV2026
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…
cs.CV2026
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…