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Jingwen Hou

3 accepted papers

2024

Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models

CVPR 2024poster

Multi-modality large language models (MLLMs) as represented by GPT-4V have introduced a paradigm shift for visual perception and understanding tasks that a variety of abilities can be achieved within one foundation model. While current MLLMs demonstrate primary low-level visual abilities from the id…

2023

Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives

ICCV 2023poster

The rapid increase in user-generated-content (UGC) videos calls for the development of effective video quality assessment (VQA) algorithms. However, the objective of the UGC-VQA problem is still ambiguous and can be viewed from two perspectives: the technical perspective, measuring the perception of…

Cited by 162PDFcodeScholar
2022

FAST-VQA: Efficient End-to-End Video Quality Assessment with Fragment Sampling

ECCV 2022poster

"Current deep video quality assessment (VQA) methods are usually with high computational costs when evaluating high-resolution videos. This cost hinders them from learning better video-quality-related representations via end-to-end training. Existing approaches typically consider naive sampling to r…