6 citations · 7 across the 4 of their papers we have counts for
5 papers
Visualizing Distribution Coverage in Generative Diffusion Models
Yifei Wang, Xiaoyu Wu, Tsu-Jui Fu +4
Diffusion distillation is widely adopted to accelerate sampling, and the resulting few-step models are broadly believed to match or even surpass their multi-step teachers in genera…
Taming Outlier Tokens in Diffusion Transformers
Xiaoyu Wu, Yifei Wang, Tsu-Jui Fu +3
We study outlier tokens in Diffusion Transformers (DiTs) for image generation. Prior work has shown that Vision Transformers (ViTs) can produce a small number of high-norm tokens t…
SPFormer: Enhancing Vision Transformer with Superpixel Representation
Jieru Mei, Liang-Chieh Chen, Alan Yuille +1
In this work, we introduce SPFormer, a novel Vision Transformer enhanced by superpixel representation. Addressing the limitations of traditional Vision Transformers' fixed-size, no…
PolyMaX: General Dense Prediction with Mask Transformer
Xuan Yang, Liangzhe Yuan, Kimberly Wilber +8
Dense prediction tasks, such as semantic segmentation, depth estimation, and surface normal prediction, can be easily formulated as per-pixel classification (discrete outputs) or r…
Superpixel Transformers for Efficient Semantic Segmentation
Alex Zihao Zhu, Jieru Mei, Siyuan Qiao +4
Semantic segmentation, which aims to classify every pixel in an image, is a key task in machine perception, with many applications across robotics and autonomous driving. Due to th…