9 citations · 29 across the 33 of their papers we have counts for
8 papers · 1 filter
OBI-Bench: Can LMMs Aid in Study of Ancient Script on Oracle Bones?
Zijian Chen, Tingzhu Chen, Wenjun Zhang +1
We introduce OBI-Bench, a holistic benchmark crafted to systematically evaluate large multi-modal models (LMMs) on whole-process oracle bone inscriptions (OBI) processing tasks dem…
Q-Bench-Video: Benchmarking the Video Quality Understanding of LMMs
Zicheng Zhang, Ziheng Jia, Haoning Wu +8
With the rising interest in research on Large Multi-modal Models (LMMs) for video understanding, many studies have emphasized general video comprehension capabilities, neglecting t…
Assessing UHD Image Quality from Aesthetics, Distortions, and Saliency
Wei Sun, Weixia Zhang, Yuqin Cao +6
UHD images, typically with resolutions equal to or higher than 4K, pose a significant challenge for efficient image quality assessment (IQA) algorithms, as adopting full-resolution…
SG-JND: Semantic-Guided Just Noticeable Distortion Predictor For Image Compression
Linhan Cao, Wei Sun, Xiongkuo Min +8
Just noticeable distortion (JND), representing the threshold of distortion in an image that is minimally perceptible to the human visual system (HVS), is crucial for image compress…
Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Model
Zhichao Zhang, Wei Sun, Xinyue Li +9
In recent years, artificial intelligence (AI)-driven video generation has gained significant attention. Consequently, there is a growing need for accurate video quality assessment…
GAIA: Rethinking Action Quality Assessment for AI-Generated Videos
Zijian Chen, Wei Sun, Yuan Tian +7
Assessing action quality is both imperative and challenging due to its significant impact on the quality of AI-generated videos, further complicated by the inherently ambiguous nat…
A-Bench: Are LMMs Masters at Evaluating AI-generated Images?
Zicheng Zhang, Haoning Wu, Chunyi Li +7
How to accurately and efficiently assess AI-generated images (AIGIs) remains a critical challenge for generative models. Given the high costs and extensive time commitments require…
AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
Marcos V. Conde, Saman Zadtootaghaj, Nabajeet Barman +33
This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based method…