Summary of Memo-bench: a Multiple Benchmark For Text-to-image and Multimodal Large Language Models on Human Emotion Analysis, by Yingjie Zhou et al.
MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis
by Yingjie Zhou, Zicheng Zhang, Jiezhang Cao, Jun Jia, Yanwei Jiang, Farong Wen, Xiaohong Liu, Xiongkuo Min, Guangtao Zhai
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the ability of AI models to understand and express human emotions. Two primary classes of AI models are generative models and Multimodal Large Language Models (MLLMs). A comprehensive benchmark called MEMO-Bench is introduced, consisting of 7,145 portraits depicting six different emotions generated by 12 Text-to-Image (T2I) models. The benchmark provides a framework for evaluating both T2I models and MLLMs in sentiment analysis. The results show that existing T2I models are more effective at generating positive emotions than negative ones. Although MLLMs can recognize human emotions, they fall short of human-level accuracy, particularly in fine-grained emotion analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI researchers want to know if computers can really understand and show emotions like humans do. To test this, they looked at two kinds of AI models: ones that make pictures (generative models) and ones that understand language (Multimodal Large Language Models). They created a special set of images showing different emotions, and used it to compare how well these models can recognize those emotions. The results show that some models are good at making happy pictures, but not so great at making sad or angry ones. Other models can tell when someone is happy or sad, but they’re not as good as humans at figuring out the tiny differences between emotions. |
