343 citations · 805 across the 37 of their papers we have counts for
39 papers
iBARLE: imBalance-Aware Room Layout Estimation
Taotao Jing, Lichen Wang, Naji Khosravan +4
Room layout estimation predicts layouts from a single panorama. It requires datasets with large-scale and diverse room shapes to train the models. However, there are significant im…
Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptation
Janet Wang, Yunbei Zhang, Zhengming Ding +1
The development of reliable and fair diagnostic systems is often constrained by the scarcity of labeled data. To address this challenge, our work explores the feasibility of unsupe…
Angle Basis: a Generative Model and Decomposition for Functional Connectivity
Anton Orlichenko, Gang Qu, Ziyu Zhou +2
Functional connectivity (FC) is one of the most common inputs to fMRI-based predictive models, due to a combination of its simplicity and robustness. However, there may be a lack o…
CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data
Chen Zhao, Anqi Liu, Xiao Zhang +6
Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data. Each omics technique only provides a limited vi…
IDA: Informed Domain Adaptive Semantic Segmentation
Zheng Chen, Zhengming Ding, Jason M. Gregory +1
Mixup-based data augmentation has been validated to be a critical stage in the self-training framework for unsupervised domain adaptive semantic segmentation (UDA-SS), which aims t…
Visualizing Transferred Knowledge: An Interpretive Model of Unsupervised Domain Adaptation
Wenxiao Xiao, Zhengming Ding, Hongfu Liu
Many research efforts have been committed to unsupervised domain adaptation (DA) problems that transfer knowledge learned from a labeled source domain to an unlabeled target domain…
Adversarial Bi-Regressor Network for Domain Adaptive Regression
Haifeng Xia, Pu Perry Wang, Toshiaki Koike-Akino +3
Domain adaptation (DA) aims to transfer the knowledge of a well-labeled source domain to facilitate unlabeled target learning. When turning to specific tasks such as indoor (Wi-Fi)…
RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation
Qucheng Peng, Zhengming Ding, Lingjuan Lyu +2
Source-Free domain adaptation transits the source-trained model towards target domain without exposing the source data, trying to dispel these concerns about data privacy and secur…
Learnable Visual Words for Interpretable Image Recognition
Wenxiao Xiao, Zhengming Ding, Hongfu Liu
To interpret deep models' predictions, attention-based visual cues are widely used in addressing \textit{why} deep models make such predictions. Beyond that, the current research c…
On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation
Wenju Zhang, Xiang Zhang, Qing Liao +5
Nuclear norm maximization has shown the power to enhance the transferability of unsupervised domain adaptation model (UDA) in an empirical scheme. In this paper, we identify a new…
PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions
Taotao Jing, Tina Chen, Renran Tian +5
Accurately modeling pedestrian intention and understanding driver decision-making processes are critical for the development of safe and socially aware autonomous driving systems.…
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning
Matias Mendieta, Taojiannan Yang, Pu Wang +3
Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices). However, the data distributi…
Towards Novel Target Discovery Through Open-Set Domain Adaptation
Taotao Jing, Hongfu Liu, Zhengming Ding
Open-set domain adaptation (OSDA) considers that the target domain contains samples from novel categories unobserved in external source domain. Unfortunately, existing OSDA methods…
3D Human Pose Estimation with Spatial and Temporal Transformers
Ce Zheng, Sijie Zhu, Matias Mendieta +3
Transformer architectures have become the model of choice in natural language processing and are now being introduced into computer vision tasks such as image classification, objec…
A Unified Joint Maximum Mean Discrepancy for Domain Adaptation
Wei Wang, Baopu Li, Shuhui Yang +6
Domain adaptation has received a lot of attention in recent years, and many algorithms have been proposed with impressive progress. However, it is still not fully explored concerni…
Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation
Jiahua Dong, Yang Cong, Gan Sun +3
Weakly-supervised learning has attracted growing research attention on medical lesions segmentation due to significant saving in pixel-level annotation cost. However, 1) most exist…
Generative Partial Visual-Tactile Fused Object Clustering
Tao Zhang, Yang Cong, Gan Sun +3
Visual-tactile fused sensing for object clustering has achieved significant progresses recently, since the involvement of tactile modality can effectively improve clustering perfor…
Towards Fair Knowledge Transfer for Imbalanced Domain Adaptation
Taotao Jing, Bingrong Xu, Jingjing Li +1
Domain adaptation (DA) becomes an up-and-coming technique to address the insufficient or no annotation issue by exploiting external source knowledge. Existing DA algorithms mainly…
Adversarial Dual Distinct Classifiers for Unsupervised Domain Adaptation
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. Conventio…
Adaptively-Accumulated Knowledge Transfer for Partial Domain Adaptation
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.…
Discriminative Cross-Domain Feature Learning for Partial Domain Adaptation
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…
Simultaneous Semantic Alignment Network for Heterogeneous Domain Adaptation
Shuang Li, Binhui Xie, Jiashu Wu +3
Heterogeneous domain adaptation (HDA) transfers knowledge across source and target domains that present heterogeneities e.g., distinct domain distributions and difference in featur…
Rethink Maximum Mean Discrepancy for Domain Adaptation
Wei Wang, Haojie Li, Zhengming Ding +1
Existing domain adaptation methods aim to reduce the distributional difference between the source and target domains and respect their specific discriminative information, by estab…
Mining Label Distribution Drift in Unsupervised Domain Adaptation
Peizhao Li, Zhengming Ding, Hongfu Liu
Unsupervised domain adaptation targets to transfer task-related knowledge from labeled source domain to unlabeled target domain. Although tremendous efforts have been made to minim…