3 citations · 4 across the 4 of their papers we have counts for
6 papers · 1 filter
Efficient Generalization Improvement Guided by Random Weight Perturbation
Tao Li, Weihao Yan, Zehao Lei +4
To fully uncover the great potential of deep neural networks (DNNs), various learning algorithms have been developed to improve the model's generalization ability. Recently, sharpn…
Autoregressive 3D Shape Generation via Canonical Mapping
An-Chieh Cheng, Xueting Li, Sifei Liu +2
With the capacity of modeling long-range dependencies in sequential data, transformers have shown remarkable performances in a variety of generative tasks such as image, audio, and…
Exploring and Improving Mobile Level Vision Transformers
Pengguang Chen, Yixin Chen, Shu Liu +2
We study the vision transformer structure in the mobile level in this paper, and find a dramatic performance drop. We analyze the reason behind this phenomenon, and propose a novel…
Deep View Synthesis via Self-Consistent Generative Network
Zhuoman Liu, Wei Jia, Ming Yang +3
View synthesis aims to produce unseen views from a set of views captured by two or more cameras at different positions. This task is non-trivial since it is hard to conduct pixel-l…
Diverse Image-to-Image Translation via Disentangled Representations
Hsin-Ying Lee, Hung-Yu Tseng, Jia-Bin Huang +2
Image-to-image translation aims to learn the mapping between two visual domains. There are two main challenges for many applications: 1) the lack of aligned training pairs and 2) m…
Instance-level Human Parsing via Part Grouping Network
Ke Gong, Xiaodan Liang, Yicheng Li +3
Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing…