10 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…
ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding
Jovana Kondic, Pengyuan Li, Dhiraj Joshi +24
Understanding charts requires models to jointly reason over geometric visual patterns, structured numerical data, and natural language -- a capability where current vision-language…
DAVE: A VLM Vision Encoder for Document Understanding and Web Agents
Brandon Huang, Hang Hua, Zhuoran Yu +3
While Vision-language models (VLMs) have demonstrated remarkable performance across multi-modal tasks, their choice of vision encoders presents a fundamental weakness: their low-le…
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…
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…
TULIP: Towards Unified Language-Image Pretraining
Zineng Tang, Long Lian, Seun Eisape +6
Despite the recent success of image-text contrastive models like CLIP and SigLIP, these models often struggle with vision-centric tasks that demand high-fidelity image understandin…