no code implementations • 9 Aug 2021 • Aidan Curtis, Xiaolin Fang, Leslie Pack Kaelbling, Tomás Lozano-Pérez, Caelan Reed Garrett
We present a strategy for designing and building very general robot manipulation systems involving the integration of a general-purpose task-and-motion planner with engineered and learned perception modules that estimate properties and affordances of unknown objects.
no code implementations • 7 Mar 2021 • Jianwen Xie, Zilong Zheng, Xiaolin Fang, Song-Chun Zhu, Ying Nian Wu
This paper studies the unsupervised cross-domain translation problem by proposing a generative framework, in which the probability distribution of each domain is represented by a generative cooperative network that consists of an energy-based model and a latent variable model.
no code implementations • 7 Feb 2019 • Jianwen Xie, Zilong Zheng, Xiaolin Fang, Song-Chun Zhu, Ying Nian Wu
This paper studies the problem of learning the conditional distribution of a high-dimensional output given an input, where the output and input may belong to two different domains, e. g., the output is a photo image and the input is a sketch image.
1 code implementation • 28 Dec 2018 • Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu
This paper proposes the divergence triangle as a framework for joint training of generator model, energy-based model and inference model.
1 code implementation • CVPR 2018 • Hao-Shu Fang, Guansong Lu, Xiaolin Fang, Jianwen Xie, Yu-Wing Tai, Cewu Lu
In this paper, we present a novel method to generate synthetic human part segmentation data using easily-obtained human keypoint annotations.
Ranked #4 on Human Part Segmentation on PASCAL-Part (using extra training data)
no code implementations • CVPR 2018 • Bowen Pan, Wuwei Lin, Xiaolin Fang, Chaoqin Huang, Bolei Zhou, Cewu Lu
Deep convolutional neural networks (CNNs) have made impressive progress in many video recognition tasks such as video pose estimation and video object detection.