2 citations · 3 across the 7 of their papers we have counts for
7 papers
SAGE: Sink-Aware Grounded Decoding for Multimodal Hallucination Mitigation
Tripti Shukla, Zsolt Kira
Large vision-language models (VLMs) frequently suffer from hallucinations, generating content that is inconsistent with visual inputs. Existing methods typically address this probl…
Taming LLMs with Negative Samples: A Reference-Free Framework to Evaluate Presentation Content with Actionable Feedback
Ananth Muppidi, Tarak Das, Sambaran Bandyopadhyay +2
The generation of presentation slides automatically is an important problem in the era of generative AI. This paper focuses on evaluating multimodal content in presentation slides…
Design-o-meter: Towards Evaluating and Refining Graphic Designs
Sahil Goyal, Abhinav Mahajan, Swasti Mishra +4
Graphic designs are an effective medium for visual communication. They range from greeting cards to corporate flyers and beyond. Off-late, machine learning techniques are able to g…
Test-time Conditional Text-to-Image Synthesis Using Diffusion Models
Tripti Shukla, Srikrishna Karanam, Balaji Vasan Srinivasan
We consider the problem of conditional text-to-image synthesis with diffusion models. Most recent works need to either finetune specific parts of the base diffusion model or introd…
An Image is Worth Multiple Words: Multi-attribute Inversion for Constrained Text-to-Image Synthesis
Aishwarya Agarwal, Srikrishna Karanam, Tripti Shukla +1
We consider the problem of constraining diffusion model outputs with a user-supplied reference image. Our key objective is to extract multiple attributes (e.g., color, object, layo…
Iterative Multi-granular Image Editing using Diffusion Models
K J Joseph, Prateksha Udhayanan, Tripti Shukla +4
Recent advances in text-guided image synthesis has dramatically changed how creative professionals generate artistic and aesthetically pleasing visual assets. To fully support such…
SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detection
Divya Kothandaraman, Sumit Shekhar, Abhilasha Sancheti +3
We present a novel method, SALAD, for the challenging vision task of adapting a pre-trained "source" domain network to a "target" domain, with a small budget for annotation in the…