4 citations · 17 across the 34 of their papers we have counts for
16 papers · 1 filter
Latent Implicit Visual Reasoning
Kelvin Li, Chuyi Shang, Leonid Karlinsky +3
While Large Multimodal Models (LMMs) have made significant progress, they remain largely text-centric, relying on language as their core reasoning modality. As a result, they are l…
PRISMM-Bench: A Benchmark of Peer-Review Grounded Multimodal Inconsistencies
Lukas Selch, Yufang Hou, M. Jehanzeb Mirza +4
Large Multimodal Models (LMMs) are increasingly applied to scientific research, yet it remains unclear whether they can reliably understand and reason over the multimodal complexit…
TTRV: Test-Time Reinforcement Learning for Vision Language Models
Akshit Singh, Shyam Marjit, Wei Lin +7
Existing methods for extracting reward signals in Reinforcement Learning typically rely on labeled data and dedicated training splits, a setup that contrasts with how humans learn…
Activation Reward Models for Few-Shot Model Alignment
Tianning Chai, Chancharik Mitra, Brandon Huang +8
Aligning Large Language Models (LLMs) and Large Multimodal Models (LMMs) to human preferences is a central challenge in improving the quality of the models' generative outputs for…
Instructify: Demystifying Metadata to Visual Instruction Tuning Data Conversion
Jacob Hansen, Wei Lin, Junmo Kang +6
Visual Instruction Tuning (VisIT) data, commonly available as human-assistant conversations with images interleaved in the human turns, are currently the most widespread vehicle fo…
Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence
Granite Vision Team, Leonid Karlinsky, Assaf Arbelle +60
We introduce Granite Vision, a lightweight large language model with vision capabilities, specifically designed to excel in enterprise use cases, particularly in visual document un…
: Bimodal Online Test-Time Adaptation for CLIP
Sarthak Kumar Maharana, Baoming Zhang, Leonid Karlinsky +2
Although open-vocabulary classification models like Contrastive Language Image Pretraining (CLIP) have demonstrated strong zero-shot learning capabilities, their robustness to comm…
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…
Teaching VLMs to Localize Specific Objects from In-context Examples
Sivan Doveh, Nimrod Shabtay, Wei Lin +9
Vision-Language Models (VLMs) have shown remarkable capabilities across diverse visual tasks, including image recognition, video understanding, and Visual Question Answering (VQA)…
GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models
M. Jehanzeb Mirza, Mengjie Zhao, Zhuoyuan Mao +12
In this work, we propose GLOV, which enables Large Language Models (LLMs) to act as implicit optimizers for Vision-Language Models (VLMs) to enhance downstream vision tasks. GLOV p…
Comparison Visual Instruction Tuning
Wei Lin, Muhammad Jehanzeb Mirza, Sivan Doveh +4
Comparing two images in terms of Commonalities and Differences (CaD) is a fundamental human capability that forms the basis of advanced visual reasoning and interpretation. It is e…
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…
Learning Human Action Recognition Representations Without Real Humans
Howard Zhong, Samarth Mishra, Donghyun Kim +7
Pre-training on massive video datasets has become essential to achieve high action recognition performance on smaller downstream datasets. However, most large-scale video datasets…
LangNav: Language as a Perceptual Representation for Navigation
Bowen Pan, Rameswar Panda, SouYoung Jin +4
We explore the use of language as a perceptual representation for vision-and-language navigation (VLN), with a focus on low-data settings. Our approach uses off-the-shelf vision sy…
TAP: Targeted Prompting for Task Adaptive Generation of Textual Training Instances for Visual Classification
M. Jehanzeb Mirza, Leonid Karlinsky, Wei Lin +3
Vision and Language Models (VLMs), such as CLIP, have enabled visual recognition of a potentially unlimited set of categories described by text prompts. However, for the best visua…
Unsupervised Domain Generalization by Learning a Bridge Across Domains
Sivan Harary, Eli Schwartz, Assaf Arbelle +11
The ability to generalize learned representations across significantly different visual domains, such as between real photos, clipart, paintings, and sketches, is a fundamental cap…