5 papers
Q-Mirror: Unlocking the Multi-Modal Potential of Scientific Text-Only QA Pairs
Junying Wang, Zicheng Zhang, Ye Shen +8
High-quality, multi-modal benchmarks are crucial for advancing scientific reasoning in large models yet their manual creation is costly and unscalable. To address this bottleneck,…
Affordance Benchmark for MLLMs
Junying Wang, Wenzhe Li, Yalun Wu +6
Affordance theory suggests that environments inherently provide action possibilities shaping perception and behavior. While Multimodal Large Language Models (MLLMs) achieve strong…
The Ever-Evolving Science Exam
Junying Wang, Zicheng Zhang, Yijin Guo +9
As foundation models grow rapidly in capability and deployment, evaluating their scientific understanding becomes increasingly critical. Existing science benchmarks have made progr…
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…
Creation-MMBench: Assessing Context-Aware Creative Intelligence in MLLM
Xinyu Fang, Zhijian Chen, Kai Lan +10
Creativity is a fundamental aspect of intelligence, involving the ability to generate novel and appropriate solutions across diverse contexts. While Large Language Models (LLMs) ha…