6 citations · 8 across the 17 of their papers we have counts for
7 papers · 1 filter
Enhancing Few-Shot Vision-Language Classification with Large Multimodal Model Features
Chancharik Mitra, Brandon Huang, Tianning Chai +7
Generative Large Multimodal Models (LMMs) like LLaVA and Qwen-VL excel at a wide variety of vision-language (VL) tasks. Despite strong performance, LMMs' generative outputs are not…
In-Context Learning Enables Robot Action Prediction in LLMs
Yida Yin, Zekai Wang, Yuvan Sharma +3
Recently, Large Language Models (LLMs) have achieved remarkable success using in-context learning (ICL) in the language domain. However, leveraging the ICL capabilities within LLMs…
LLARVA: Vision-Action Instruction Tuning Enhances Robot Learning
Dantong Niu, Yuvan Sharma, Giscard Biamby +5
In recent years, instruction-tuned Large Multimodal Models (LMMs) have been successful at several tasks, including image captioning and visual question answering; yet leveraging th…
Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning
Brandon Huang, Chancharik Mitra, Assaf Arbelle +3
The recent success of interleaved Large Multimodal Models (LMMs) in few-shot learning suggests that in-context learning (ICL) with many examples can be promising for learning new t…
Navigating the Labyrinth: Evaluating LLMs' Ability to Reason About Search Problems
Nasim Borazjanizadeh, Roei Herzig, Trevor Darrell +2
Large Language Models (LLMs) have recently achieved impressive performance in math and reasoning benchmarks. However, they often struggle with logic problems and puzzles that are r…
ConMe: Rethinking Evaluation of Compositional Reasoning for Modern VLMs
Irene Huang, Wei Lin, M. Jehanzeb Mirza +11
Compositional Reasoning (CR) entails grasping the significance of attributes, relations, and word order. Recent Vision-Language Models (VLMs), comprising a visual encoder and a Lar…