1 citations · 1 across the 3 of their papers we have counts for
4 papers
Visual Tokens on Demand: Visual Budgeting for Scalable and Efficient Multi-Image Reasoning in Multimodal LLMs
Aimon Rahman, Joymallya Chakraborty, Vidit Bansal +1
Multimodal large language models have advanced rapidly, but most remain optimized for single-image inputs. Multi-image reasoning is costly because each image adds many visual token…
Universal Guideline-Driven Image Clustering via a Hybrid LLM Agent
Wenliang Zhong, Rob Barton, Lucas Goncalves +7
Unifying image clustering across different clustering scenarios remains challenging due to fundamental gaps among tasks. We introduce a Guideline-Driven Image Clustering Agent, the…
Enabling Fine-Grained Operating Points for Black-Box LLMs
Ege Beyazit, KL Navaneet, Prashant Mathur +3
Black-box Large Language Models (LLMs) provide practical and accessible alternatives to other machine learning methods, as they require minimal labeled data and machine learning ex…
Can Contrastive Learning Refine Embeddings
Lihui Liu, Jinha Kim, Vidit Bansal
Recent advancements in contrastive learning have revolutionized self-supervised representation learning and achieved state-of-the-art performance on benchmark tasks. While most exi…