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20242026
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cs.CV2026

Beyond Frame Selection: Generative Latent Evidence Aggregation for Long-Video Understanding

Bowen Liu, Shuning Wang, Xinpeng Ding +3

Long-video understanding commonly compresses videos into a small set of frames or visual tokens for answer generation. Existing compact pipelines focus on retaining relevant visual…

cs.CV2026

MedHorizon: Towards Long-context Medical Video Understanding in the Wild

Bodong Du, Bowen Liu, Yang Yu +8

Medical multimodal large language models (MLLMs) have advanced image understanding and short-video analysis, but real clinical review often requires full-procedure video understand…

cs.CV2026

DenseMLLM: Standard Multimodal LLMs for Dense Prediction

Yi Li, Hongze Shen, Lexiang Tang +6

Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in high-level visual understanding. However, extending these models to fine-grained dense predic…

cs.CV2025

Token Activation Map to Visually Explain Multimodal LLMs

Yi Li, Hualiang Wang, Xinpeng Ding +2

Multimodal large language models (MLLMs) are broadly empowering various fields. Despite their advancements, the explainability of MLLMs remains less explored, hindering deeper unde…

cs.CV2025

PaMi-VDPO: Mitigating Video Hallucinations by Prompt-Aware Multi-Instance Video Preference Learning

Xinpeng Ding, Kui Zhang, Jianhua Han +3

Direct Preference Optimization (DPO) helps reduce hallucinations in Video Multimodal Large Language Models (VLLMs), but its reliance on offline preference data limits adaptability…

cs.CV2024

HiRes-LLaVA: Restoring Fragmentation Input in High-Resolution Large Vision-Language Models

Runhui Huang, Xinpeng Ding, Chunwei Wang +7

High-resolution inputs enable Large Vision-Language Models (LVLMs) to discern finer visual details, enhancing their comprehension capabilities. To reduce the training and computati…