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cs.CV2025

SPIKE-RL: Video-LLMs meet Bayesian Surprise

Sahithya Ravi, Aditya Chinchure, Raymond T. Ng +2

Real-world videos often show routine activities punctuated by memorable, surprising events. However, most Video-LLMs process videos by sampling frames uniformly, likely missing cri…

cs.CV2025

Mitigate One, Skew Another? Tackling Intersectional Biases in Text-to-Image Models

Pushkar Shukla, Aditya Chinchure, Emily Diana +5

The biases exhibited by text-to-image (TTI) models are often treated as independent, though in reality, they may be deeply interrelated. Addressing bias along one dimension - such…

cs.CV2025

Black Swan: Abductive and Defeasible Video Reasoning in Unpredictable Events

Aditya Chinchure, Sahithya Ravi, Raymond Ng +3

The commonsense reasoning capabilities of vision-language models (VLMs), especially in abductive reasoning and defeasible reasoning, remain poorly understood. Most benchmarks focus…

cs.CV2025

BiasConnect: Investigating Bias Interactions in Text-to-Image Models

Pushkar Shukla, Aditya Chinchure, Emily Diana +5

The biases exhibited by Text-to-Image (TTI) models are often treated as if they are independent, but in reality, they may be deeply interrelated. Addressing bias along one dimensio…

cs.CV2024

TIBET: Identifying and Evaluating Biases in Text-to-Image Generative Models

Aditya Chinchure, Pushkar Shukla, Gaurav Bhatt +4

Text-to-Image (TTI) generative models have shown great progress in the past few years in terms of their ability to generate complex and high-quality imagery. At the same time, thes…