activity
20162023
most citedVisual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapes

93 citations · 375 across the 45 of their papers we have counts for

collaborators
Showing 2022Show all

15 papers · 1 filter

cs.CV2022★ 14 cited

Executing your Commands via Motion Diffusion in Latent Space

Xin Chen, Biao Jiang, Wen Liu +5

We study a challenging task, conditional human motion generation, which produces plausible human motion sequences according to various conditional inputs, such as action classes or…

cs.CV2022★ 2 cited

Instance-aware Model Ensemble With Distillation For Unsupervised Domain Adaptation

Weimin Wu, Jiayuan Fan, Tao Chen +3

The linear ensemble based strategy, i.e., averaging ensemble, has been proposed to improve the performance in unsupervised domain adaptation tasks. However, a typical UDA task is u…

cs.HC2022

Cross-Subject Emotion Recognition with Sparsely-Labeled Peripheral Physiological Data Using SHAP-Explained Tree Ensembles

Feng Zhou, Tao Chen, Baiying Lei

There are still many challenges of emotion recognition using physiological data despite the substantial progress made recently. In this paper, we attempted to address two major cha…

cs.RO2022★ 93 cited

Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapes

Tao Chen, Megha Tippur, Siyang Wu +3

In-hand object reorientation is necessary for performing many dexterous manipulation tasks, such as tool use in less structured environments that remain beyond the reach of current…

cs.CV2022★ 6 cited

Coordinates Are NOT Lonely -- Codebook Prior Helps Implicit Neural 3D Representations

Fukun Yin, Wen Liu, Zilong Huang +3

Implicit neural 3D representation has achieved impressive results in surface or scene reconstruction and novel view synthesis, which typically uses the coordinate-based multi-layer…

cs.CV2022★ 3 cited

Stimulative Training of Residual Networks: A Social Psychology Perspective of Loafing

Peng Ye, Shengji Tang, Baopu Li +2

Residual networks have shown great success and become indispensable in today's deep models. In this work, we aim to re-investigate the training process of residual networks from a…