216 citations · 779 across the 38 of their papers we have counts for
8 papers · 1 filter
Self-supervised Autoregressive Domain Adaptation for Time Series Data
Mohamed Ragab, Emadeldeen Eldele, Zhenghua Chen +3
Unsupervised domain adaptation (UDA) has successfully addressed the domain shift problem for visual applications. Yet, these approaches may have limited performance for time series…
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer
Yining Ma, Jingwen Li, Zhiguang Cao +4
Recently, Transformer has become a prevailing deep architecture for solving vehicle routing problems (VRPs). However, it is less effective in learning improvement models for VRP be…
Multi-Source Video Domain Adaptation with Temporal Attentive Moment Alignment
Yuecong Xu, Jianfei Yang, Haozhi Cao +4
Multi-Source Domain Adaptation (MSDA) is a more practical domain adaptation scenario in real-world scenarios. It relaxes the assumption in conventional Unsupervised Domain Adaptati…
Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Network
Yuecong Xu, Jianfei Yang, Haozhi Cao +3
Partial Domain Adaptation (PDA) is a practical and general domain adaptation scenario, which relaxes the fully shared label space assumption such that the source label space subsum…
Attention-Guided NIR Image Colorization via Adaptive Fusion of Semantic and Texture Clues
Xingxing Yang, Jie Chen, Zaifeng Yang +1
Near infrared (NIR) imaging has been widely applied in low-light imaging scenarios; however, it is difficult for human and algorithms to perceive the real scene in the colorless NI…
ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +4
Sleep staging is of great importance in the diagnosis and treatment of sleep disorders. Recently, numerous data-driven deep learning models have been proposed for automatic sleep s…
Time-Series Representation Learning via Temporal and Contextual Contrasting
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +4
Learning decent representations from unlabeled time-series data with temporal dynamics is a very challenging task. In this paper, we propose an unsupervised Time-Series representat…
Deep Inertial Odometry with Accurate IMU Preintegration
Rooholla Khorrambakht, Chris Xiaoxuan Lu, Hamed Damirchi +2
Inertial Measurement Units (IMUs) are interceptive modalities that provide ego-motion measurements independent of the environmental factors. They are widely adopted in various auto…