4 citations · 7 across the 12 of their papers we have counts for
12 papers
VDiff-Bench: A Challenging Benchmark for Fine-Grained Image Difference Identification
Yixin Wan, Tianle Zheng, Kai-Wei Chang
Multimodal Large Language Models (MLLMs) perform strongly on general visual understanding tasks such as visual question answering, yet they often struggle with a basic comparative…
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…
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…
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…
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…
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…
The Factuality Tax of Diversity-Intervened Text-to-Image Generation: Benchmark and Fact-Augmented Intervention
Yixin Wan, Di Wu, Haoran Wang +1
Prompt-based "diversity interventions" are commonly adopted to improve the diversity of Text-to-Image (T2I) models depicting individuals with various racial or gender traits. Howev…
MetaKP: On-Demand Keyphrase Generation
Di Wu, Xiaoxian Shen, Kai-Wei Chang
Traditional keyphrase prediction methods predict a single set of keyphrases per document, failing to cater to the diverse needs of users and downstream applications. To bridge the…
MACAROON: Training Vision-Language Models To Be Your Engaged Partners
Shujin Wu, Yi R. Fung, Sha Li +3
Large vision-language models (LVLMs), while proficient in following instructions and responding to diverse questions, invariably generate detailed responses even when questions are…
Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation
Yixin Wan, Arjun Subramonian, Anaelia Ovalle +6
The recent advancement of large and powerful models with Text-to-Image (T2I) generation abilities -- such as OpenAI's DALLE-3 and Google's Gemini -- enables users to generate high-…
White Men Lead, Black Women Help? Benchmarking and Mitigating Language Agency Social Biases in LLMs
Yixin Wan, Kai-Wei Chang
Social biases can manifest in language agency. However, very limited research has investigated such biases in Large Language Model (LLM)-generated content. In addition, previous wo…
The Male CEO and the Female Assistant: Evaluation and Mitigation of Gender Biases in Text-To-Image Generation of Dual Subjects
Yixin Wan, Kai-Wei Chang
Recent large-scale T2I models like DALLE-3 have made progress in reducing gender stereotypes when generating single-person images. However, significant biases remain when generatin…