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cs.CV2026

Uncertainty-Aware Knowledge Distillation for Multimodal Large Language Models

Jingchen Sun, Shaobo Han, Deep Patel +3

Knowledge distillation establishes a learning paradigm that leverages both data supervision and teacher guidance. However, determining the optimal balance between learning from dat…

cs.CV2025

VisR-Bench: An Empirical Study on Visual Retrieval-Augmented Generation for Multilingual Long Document Understanding

Jian Chen, Ming Li, Jihyung Kil +6

Most organizational data in this world are stored as documents, and visual retrieval plays a crucial role in unlocking the collective intelligence from all these documents. However…

cs.CV2025

Multimodal LLMs as Customized Reward Models for Text-to-Image Generation

Shijie Zhou, Ruiyi Zhang, Huaisheng Zhu +5

We introduce LLaVA-Reward, an efficient reward model designed to automatically evaluate text-to-image (T2I) generations across multiple perspectives, leveraging pretrained multimod…

cs.CV2025

MusiXQA: Advancing Visual Music Understanding in Multimodal Large Language Models

Jian Chen, Wenye Ma, Penghang Liu +7

Multimodal Large Language Models (MLLMs) have achieved remarkable visual reasoning abilities in natural images, text-rich documents, and graphic designs. However, their ability to…

cs.CV2024

A High-Quality Text-Rich Image Instruction Tuning Dataset via Hybrid Instruction Generation

Shijie Zhou, Ruiyi Zhang, Yufan Zhou +1

Large multimodal models still struggle with text-rich images because of inadequate training data. Self-Instruct provides an annotation-free way for generating instruction data, but…

cs.CV2024

SV-RAG: LoRA-Contextualizing Adaptation of MLLMs for Long Document Understanding

Jian Chen, Ruiyi Zhang, Yufan Zhou +6

Multimodal large language models (MLLMs) have recently shown great progress in text-rich image understanding, yet they still struggle with complex, multi-page visually-rich documen…