Jinliang Han — accepted AI-conference papers

2026

Scaling-up Perceptual Video Quality Assessment

Ziheng Jia, Zicheng Zhang, Xiaorong Zhu, Chunyi Li, Jinliang Han, Xiaohong Liu +2

AAAI 2026technical

The data scaling law has significantly enhanced large multi-modal models (LMMs) performance across various downstream tasks. However, in the domain of perceptual video quality assessment (VQA), the potential of data scaling remains unprecedented due to the scarcity of labeled resources and the insuf

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2026

VITAL: Vision-Encoder-centered Pre-training for LMMs in Visual Quality Assessment

Ziheng Jia, Linhan Cao, Jinliang Han, Zicheng Zhang, Jiaying Qian, Jiarui Wang +3

CVPR 2026

Developing a robust visual quality assessment (VQualA) large multi-modal model (LMM) requires achieving versatility, powerfulness, and transferability. However, existing VQualA LMMs typically focus on a single task and rely on full-parameter fine-tuning, which makes them prone to overfitting on spec

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