Search Results for author: Taotao Jing

Found 9 papers, 3 papers with code

PSI: A Pedestrian Behavior Dataset for Socially Intelligent Autonomous Car

2 code implementations5 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.

Autonomous Vehicles

Privacy Protected Multi-Domain Collaborative Learning

no code implementations29 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.

Unsupervised Domain Adaptation

Towards Novel Target Discovery Through Open-Set Domain Adaptation

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.

Domain Adaptation Open Set Learning

Collaborative Normalization for Unsupervised Domain Adaptation

no code implementations1 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.

Unsupervised Domain Adaptation

Towards Fair Knowledge Transfer for Imbalanced Domain Adaptation

no code implementations23 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.

Domain Adaptation Fairness +1

Adaptively-Accumulated Knowledge Transfer for Partial Domain Adaptation

no code implementations27 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.

Partial Domain Adaptation Transfer Learning

Adversarial Dual Distinct Classifiers for Unsupervised Domain Adaptation

no code implementations27 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.

Unsupervised Domain Adaptation

Discriminative Cross-Domain Feature Learning for Partial Domain Adaptation

no code implementations26 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.

Partial Domain Adaptation

EV-Action: Electromyography-Vision Multi-Modal Action Dataset

1 code implementation20 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.

Action Analysis Action Recognition +3

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