2 code implementations • 5 Dec 2021 • Tina Chen, Taotao Jing, Renran Tian, Yaobin Chen, Joshua Domeyer, Heishiro Toyoda, Rini Sherony, Zhengming Ding
These innovative labels can enable several computer vision tasks, including pedestrian intent/behavior prediction, vehicle-pedestrian interaction segmentation, and video-to-language mapping for explainable algorithms.
no code implementations • 29 Sep 2021 • Haifeng Xia, Taotao Jing, Zizhan Zheng, Zhengming Ding
Unsupervised domain adaptation (UDA) aims to transfer knowledge from one or more well-labeled source domains to improve model performance on the different-yet-related target domain without any annotations.
1 code implementation • ICCV 2021 • Taotao Jing, Hongfu Liu, Zhengming Ding
In this paper, we propose a novel framework to accurately identify the seen categories in target domain, and effectively recover the semantic attributes for unseen categories.
no code implementations • 1 Jan 2021 • Haifeng Xia, Taotao Jing, Zhengming Ding
Batch Normalization (BN) as an important component assists Deep Neural Networks achieving promising performance for extensive learning tasks by scaling distribution of feature representations within mini-batches.
no code implementations • 23 Oct 2020 • Taotao Jing, Bingrong Xu, Jingjing Li, Zhengming Ding
Such three strategies are formulated into a unified framework to address the fairness issue and domain shift challenge.
no code implementations • 27 Aug 2020 • Taotao Jing, Haifeng Xia, Zhengming Ding
Partial domain adaptation (PDA) attracts appealing attention as it deals with a realistic and challenging problem when the source domain label space substitutes the target domain.
no code implementations • 27 Aug 2020 • Taotao Jing, Zhengming Ding
Unsupervised Domain adaptation (UDA) attempts to recognize the unlabeled target samples by building a learning model from a differently-distributed labeled source domain.
no code implementations • 26 Aug 2020 • Taotao Jing, Ming Shao, Zhengming Ding
Partial domain adaptation aims to adapt knowledge from a larger and more diverse source domain to a smaller target domain with less number of classes, which has attracted appealing attention.
1 code implementation • 20 Apr 2019 • Lichen Wang, Bin Sun, Joseph Robinson, Taotao Jing, Yun Fu
To make up this, we introduce a new, large-scale EV-Action dataset in this work, which consists of RGB, depth, electromyography (EMG), and two skeleton modalities.
Ranked #4 on Multimodal Activity Recognition on EV-Action