Semi-supervised learning aims to boost the accuracy of a model by exploring unlabeled images. The state-of-the-art methods are consistency-based which learn about unlabeled images by encouraging the model to give consistent predictions for images under different augmentations. However, when applied to pose estimation, the methods degenerate and predict every pixel…


In this work, we tackle the problem of category-level online pose tracking of objects from point cloud sequences. For the first time, we propose a unified framework that can handle 9DoF pose tracking for novel rigid object instances as well as per-part pose tracking for articulated objects from known categories…


Learning an accurate model of the environment is essential for model-based control tasks. Existing methods in robotic visuomotor control usually learn from data with heavily labelled actions, object entities or locations, which can be demanding in many cases. To cope with this limitation, we propose a method, dubbed DMotion, that…


Information flow measures, over the duration of a game, the audience’s belief of who will win, and thus can reflect the amount of surprise in a game. To quantify the relationship between information flow and audiences’ perceived quality, we conduct a case study where subjects watch one of the world’s…


Finding the minimum approximate ratio for Nash equilibrium of bi-matrix games has derived a series of studies, started with 3/4, followed by 1/2, 0.38 and 0.36, finally the best approximate ratio of 0.3393 by Tsaknakis and Spirakis (TS algorithm for short). Efforts to improve the results remain not successful in…


In active visual tracking, it is notoriously difficult when distracting objects appear, as distractors often mislead the tracker by occluding the target or bringing a confusing appearance. To address this issue, we propose a mixed cooperative-competitive multi-agent game, where a target and multiple distractors form a collaborative team to play…


Co-part segmentation is an important problem in computer vision for its rich applications. We propose an unsupervised learning approach for co-part segmentation from images. For the training stage, we leverage motion information embedded in videos and explicitly extract latent representations to segment meaningful object parts. More importantly, we introduce a…


Welcome Dr. Shaofeng Jiang, Dr. He Wang, and Dr. Tongyang Li to join CFCS, Peking University as assistant professors in the summer of 2021. Read profiles here:


We develop a neural technique for articulating 3D characters using enveloping with a pre-defined skeletal structure, which is essential for animating a character with motion capture (mocap) data. …


CFCS Quantum Day, organized by Center on Frontiers of Computing Studies (CFCS), Peking University, was held virtually on May 12th, 2021. Top-level researchers in quantum computing were invited as speakers and gave stimulating talks (videos can be accessed here). Nearly 5000 people from universities such as Tsinghua University, University of…

Center on Frontiers of Computing Studies, PKU

A new initiative at Peking University. More information: https://cfcs.pku.edu.cn/english/index.htm

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