2 citations · 2 across the 4 of their papers we have counts for
5 papers
EmoGenie: Benchmarking and Evaluating Expressed and Evoked Emotion in T2I Generation with MLLMs
Zixuan Xing, Lancheng Gao, Shengyan Li +4
Rapid advances in artificial intelligence-generated content (AIGC) have significantly promoted using text-to-image (T2I) models to generate images with specific affective intention…
Emo-Bench: A Scalable Benchmark for Multimodal Evoked and Expressed Emotion Understanding via Bayesian Pairwise Alignment
Lancheng Gao, Ziheng Jia, Shengyan Li +4
Understanding both expressed and evoked emotions is critical for multimodal large language models (MLLMs) to achieve comprehensive affect-aware interactions. However, existing benc…
RAVEN-Eval: Rubric-Guided Automatic Evaluation for AI Video Generation Models Based on LMM Preference Judgement
Ziheng Jia, Jiaying Qian, Zicheng Zhang +3
AI video generation has advanced rapidly and entered widespread commercial use. As a result, quality differences among videos produced by state-of-the-art AI video generation model…
EEmo-Logic: A Unified Dataset and Multi-Stage Framework for Comprehensive Image-Evoked Emotion Assessment
Lancheng Gao, Ziheng Jia, Zixuan Xing +4
Understanding the multi-dimensional attributes and intensity nuances of image-evoked emotions is pivotal for advancing machine empathy and empowering diverse human-computer interac…
EEmo-Bench: A Benchmark for Multi-modal Large Language Models on Image Evoked Emotion Assessment
Lancheng Gao, Ziheng Jia, Yunhao Zeng +5
The furnishing of multi-modal large language models (MLLMs) has led to the emergence of numerous benchmark studies, particularly those evaluating their perception and understanding…