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cs.CV2026

FCMBench-Video: Benchmarking Document Video Intelligence

Runze Cui, Fangxin Shang, Yehui Yang +3

Document understanding is a critical capability in financial credit review, onboarding, and remote verification, where both decision accuracy and evidence traceability matter. Comp…

cs.CV2026

Revisiting Cross-Attention Mechanisms: Leveraging Beneficial Noise for Domain-Adaptive Learning

Zelin Zang, Yehui Yang, Fei Wang +2

Unsupervised Domain Adaptation (UDA) seeks to transfer knowledge from a labeled source domain to an unlabeled target domain but often suffers from severe domain and scale gaps that…

cs.CV2026

FCMBench: The First Large-scale Financial Credit Multimodal Benchmark for Real-world Applications

Yehui Yang, Dalu Yang, Fangxin Shang +7

FCMBench is the first large-scale and privacy-compliant multimodal benchmark for real-world financial credit applications, covering tasks and robustness challenges from domain spec…

cs.CV2025

Towards a Multimodal Large Language Model with Pixel-Level Insight for Biomedicine

Xiaoshuang Huang, Lingdong Shen, Jia Liu +4

In recent years, Multimodal Large Language Models (MLLM) have achieved notable advancements, demonstrating the feasibility of developing an intelligent biomedical assistant. Howeve…

cs.CV2025

DATE: Dynamic Absolute Time Enhancement for Long Video Understanding

Chao Yuan, Yang Yang, Yehui Yang +1

Long video understanding remains a fundamental challenge for multimodal large language models (MLLMs), particularly in tasks requiring precise temporal reasoning and event localiza…

cs.CV2024

A Refer-and-Ground Multimodal Large Language Model for Biomedicine

Xiaoshuang Huang, Haifeng Huang, Lingdong Shen +4

With the rapid development of multimodal large language models (MLLMs), especially their capabilities in visual chat through refer and ground functionalities, their significance is…