activity
20182021
most citedDivergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model

3 citations · 5 across the 3 of their papers we have counts for

collaborators

6 papers

cs.RO2021

Long-Horizon Manipulation of Unknown Objects via Task and Motion Planning with Estimated Affordances

Aidan Curtis, Xiaolin Fang, Leslie Pack Kaelbling +2

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…

cs.CV20212 cited

Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation

Jianwen Xie, Zilong Zheng, Xiaolin Fang +2

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 g…

stat.ML2019

Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Conditional Learning

Jianwen Xie, Zilong Zheng, Xiaolin Fang +2

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,…

stat.ML20193 cited

Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model

Tian Han, Erik Nijkamp, Xiaolin Fang +3

This paper proposes the divergence triangle as a framework for joint training of generator model, energy-based model and inference model. The divergence triangle is a compact and s…

cs.CV2018

Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer

Hao-Shu Fang, Guansong Lu, Xiaolin Fang +3

Human body part parsing, or human semantic part segmentation, is fundamental to many computer vision tasks. In conventional semantic segmentation methods, the ground truth segmenta…

cs.CV2018

Recurrent Residual Module for Fast Inference in Videos

Bowen Pan, Wuwei Lin, Xiaolin Fang +3

Deep convolutional neural networks (CNNs) have made impressive progress in many video recognition tasks such as video pose estimation and video object detection. However, CNN infer…