2 citations · 3 across the 4 of their papers we have counts for
8 papers
Efficient Scaling of Diffusion Transformers for Text-to-Image Generation
Hao Li, Shamit Lal, Zhiheng Li +9
We empirically study the scaling properties of various Diffusion Transformers (DiTs) for text-to-image generation by performing extensive and rigorous ablations, including training…
FairRAG: Fair Human Generation via Fair Retrieval Augmentation
Robik Shrestha, Yang Zou, Qiuyu Chen +3
Existing text-to-image generative models reflect or even amplify societal biases ingrained in their training data. This is especially concerning for human image generation where mo…
Discover and Mitigate Multiple Biased Subgroups in Image Classifiers
Zeliang Zhang, Mingqian Feng, Zhiheng Li +1
Machine learning models can perform well on in-distribution data but often fail on biased subgroups that are underrepresented in the training data, hindering the robustness of mode…
StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis
Zhiheng Li, Martin Renqiang Min, Kai Li +1
Although progress has been made for text-to-image synthesis, previous methods fall short of generalizing to unseen or underrepresented attribute compositions in the input text. Lac…
Discover the Unknown Biased Attribute of an Image Classifier
Zhiheng Li, Chenliang Xu
Recent works find that AI algorithms learn biases from data. Therefore, it is urgent and vital to identify biases in AI algorithms. However, the previous bias identification pipeli…
Learning a Weakly-Supervised Video Actor-Action Segmentation Model with a Wise Selection
Jie Chen, Zhiheng Li, Jiebo Luo +1
We address weakly-supervised video actor-action segmentation (VAAS), which extends general video object segmentation (VOS) to additionally consider action labels of the actors. The…