8 citations · 18 across the 11 of their papers we have counts for
16 papers
VITAL: Vision-Encoder-centered Pre-training for LMMs in Visual Quality Assessment
Ziheng Jia, Linhan Cao, Jinliang Han +6
Developing a robust visual quality assessment (VQualA) large multi-modal model (LMM) requires achieving versatility, powerfulness, and transferability. However, existing VQualA LMM…
TIT-Score: Evaluating Long-Prompt Based Text-to-Image Alignment via Text-to-Image-to-Text Consistency
Juntong Wang, Huiyu Duan, Jiarui Wang +3
With the rapid advancement of large multimodal models (LMMs), recent text-to-image (T2I) models can generate high-quality images and demonstrate great alignment to short prompts. H…
Refine-IQA: Multi-Stage Reinforcement Finetuning for Perceptual Image Quality Assessment
Ziheng Jia, Jiaying Qian, Zicheng Zhang +2
Reinforcement fine-tuning (RFT) is a proliferating paradigm for LMM training. Analogous to high-level reasoning tasks, RFT is similarly applicable to low-level vision domains, incl…
DFBench: Benchmarking Deepfake Image Detection Capability of Large Multimodal Models
Jiarui Wang, Huiyu Duan, Juntong Wang +8
With the rapid advancement of generative models, the realism of AI-generated images has significantly improved, posing critical challenges for verifying digital content authenticit…
GOBench: Benchmarking Geometric Optics Generation and Understanding of MLLMs
Xiaorong Zhu, Ziheng Jia, Jiarui Wang +6
The rapid evolution of Multi-modality Large Language Models (MLLMs) is driving significant advancements in visual understanding and generation. Nevertheless, a comprehensive assess…
Scaling-up Perceptual Video Quality Assessment
Ziheng Jia, Zicheng Zhang, Zeyu Zhang +12
The data scaling law has been shown to significantly enhance the performance of large multi-modal models (LMMs) across various downstream tasks. However, in the domain of perceptua…