activity
20242026
most citedSurvey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation

4 citations · 7 across the 12 of their papers we have counts for

collaborators
Showing 2025Show all

5 papers · 1 filter

cs.CV2025

MotionEdit: Benchmarking and Learning Motion-Centric Image Editing

Yixin Wan, Lei Ke, Wenhao Yu +2

We introduce MotionEdit, a novel dataset for motion-centric image editing-the task of modifying subject actions and interactions while preserving identity, structure, and physical…

cs.CL2025

Not Every Token Needs Forgetting: Selective Unlearning to Limit Change in Utility in Large Language Model Unlearning

Yixin Wan, Anil Ramakrishna, Kai-Wei Chang +2

Large Language Model (LLM) unlearning has recently gained significant attention, driven by the need to remove unwanted information, such as private, sensitive, or copyrighted conte…

cs.CV2025

CompAlign: Improving Compositional Text-to-Image Generation with a Complex Benchmark and Fine-Grained Feedback

Yixin Wan, Kai-Wei Chang

State-of-the-art T2I models are capable of generating high-resolution images given textual prompts. However, they still struggle with accurately depicting compositional scenes that…

cs.CV2025

VisRet: Visualization Improves Knowledge-Intensive Text-to-Image Retrieval

Di Wu, Yixin Wan, Kai-Wei Chang

Text-to-image retrieval (T2I retrieval) remains challenging because cross-modal embeddings often behave as bags of concepts, underrepresenting structured visual relationships such…

cs.CL2025

Where Fact Ends and Fairness Begins: Redefining AI Bias Evaluation through Cognitive Biases

Jen-tse Huang, Yuhang Yan, Linqi Liu +4

Recent failures such as Google Gemini generating people of color in Nazi-era uniforms illustrate how AI outputs can be factually plausible yet socially harmful. AI models are incre…