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MLLM-as-a-Judge for Image Safety without Human Labeling
Zhenting Wang, Shuming Hu, Shiyu Zhao +12
Image content safety has become a significant challenge with the rise of visual media on online platforms. Meanwhile, in the age of AI-generated content (AIGC), many image generati…
Imagine yourself: Tuning-Free Personalized Image Generation
Zecheng He, Bo Sun, Felix Juefei-Xu +14
Diffusion models have demonstrated remarkable efficacy across various image-to-image tasks. In this research, we introduce Imagine yourself, a state-of-the-art model designed for p…
T-HITL Effectively Addresses Problematic Associations in Image Generation and Maintains Overall Visual Quality
Susan Epstein, Li Chen, Alessandro Vecchiato +1
Generative AI image models may inadvertently generate problematic representations of people. Past research has noted that millions of users engage daily across the world with these…