5 citations · 6 across the 7 of their papers we have counts for
7 papers
S2H-DPO: Hardness-Aware Preference Optimization for Vision-Language Models
Nitish Shukla, Surgan Jandial, Arun Ross
Vision-Language Models (VLMs) have demonstrated remarkable progress in single-image understanding, yet effective reasoning across multiple images remains challenging. We identify a…
Towards Efficient Exemplar Based Image Editing with Multimodal VLMs
Avadhoot Jadhav, Ashutosh Srivastava, Abhinav Java +4
Text-to-Image Diffusion models have enabled a wide array of image editing applications. However, capturing all types of edits through text alone can be challenging and cumbersome.…
ReEdit: Multimodal Exemplar-Based Image Editing with Diffusion Models
Ashutosh Srivastava, Tarun Ram Menta, Abhinav Java +4
Modern Text-to-Image (T2I) Diffusion models have revolutionized image editing by enabling the generation of high-quality photorealistic images. While the de facto method for perfor…
Thinking Fair and Slow: On the Efficacy of Structured Prompts for Debiasing Language Models
Shaz Furniturewala, Surgan Jandial, Abhinav Java +4
Existing debiasing techniques are typically training-based or require access to the model's internals and output distributions, so they are inaccessible to end-users looking to ada…
Introducing v0.5 of the AI Safety Benchmark from MLCommons
Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed +97
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safe…
LEAST: "Local" text-conditioned image style transfer
Silky Singh, Surgan Jandial, Simra Shahid +1
Text-conditioned style transfer enables users to communicate their desired artistic styles through text descriptions, offering a new and expressive means of achieving stylization.…
All Should Be Equal in the Eyes of Language Models: Counterfactually Aware Fair Text Generation
Pragyan Banerjee, Abhinav Java, Surgan Jandial +4
Fairness in Language Models (LMs) remains a longstanding challenge, given the inherent biases in training data that can be perpetuated by models and affect the downstream tasks. Re…