149 citations · 450 across the 17 of their papers we have counts for
17 papers
Feature-aware conditional GAN for category text generation
Xinze Li, Kezhi Mao, Fanfan Lin +1
Category text generation receives considerable attentions since it is beneficial for various natural language processing tasks. Recently, the generative adversarial network (GAN) h…
Particle swarm optimization with state-based adaptive velocity limit strategy
Xinze Li, Kezhi Mao, Fanfan Lin +1
Velocity limit (VL) has been widely adopted in many variants of particle swarm optimization (PSO) to prevent particles from searching outside the solution space. Several adaptive V…
Artificial-Intelligence-Based Triple Phase Shift Modulation for Dual Active Bridge Converter with Minimized Current Stress
Xinze Li, Xin Zhang, Fanfan Lin +2
The dual active bridge (DAB) converter has been popular in many applications for its outstanding power density and bidirectional power transfer capacity. Up to now, triple phase sh…
Artificial-Intelligence-Based Hybrid Extended Phase Shift Modulation for the Dual Active Bridge Converter with Full ZVS Range and Optimal Efficiency
Xinze Li, Xin Zhang, Fanfan Lin +2
Dual active bridge (DAB) converter is the key enabler in many popular applications such as wireless charging, electric vehicle and renewable energy. ZVS range and efficiency are tw…
Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series Data
Qing Xu, Min Wu, Xiaoli Li +2
For many real-world time series tasks, the computational complexity of prevalent deep leaning models often hinders the deployment on resource-limited environments (e.g., smartphone…
Confidence Attention and Generalization Enhanced Distillation for Continuous Video Domain Adaptation
Xiyu Wang, Yuecong Xu, Jianfei Yang +2
Continuous Video Domain Adaptation (CVDA) is a scenario where a source model is required to adapt to a series of individually available changing target domains continuously without…
Calibrating Class Weights with Multi-Modal Information for Partial Video Domain Adaptation
Xiyu Wang, Yuecong Xu, Kezhi Mao +1
Assuming the source label space subsumes the target one, Partial Video Domain Adaptation (PVDA) is a more general and practical scenario for cross-domain video classification probl…
Self-Supervised Video Representation Learning by Video Incoherence Detection
Haozhi Cao, Yuecong Xu, Jianfei Yang +4
This paper introduces a novel self-supervised method that leverages incoherence detection for video representation learning. It roots from the observation that visual systems of hu…
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…
Aligning Correlation Information for Domain Adaptation in Action Recognition
Yuecong Xu, Jianfei Yang, Haozhi Cao +3
Domain adaptation (DA) approaches address domain shift and enable networks to be applied to different scenarios. Although various image DA approaches have been proposed in recent y…
Effective Action Recognition with Embedded Key Point Shifts
Haozhi Cao, Yuecong Xu, Jianfei Yang +3
Temporal feature extraction is an essential technique in video-based action recognition. Key points have been utilized in skeleton-based action recognition methods but they require…
PNL: Efficient Long-Range Dependencies Extraction with Pyramid Non-Local Module for Action Recognition
Yuecong Xu, Haozhi Cao, Jianfei Yang +3
Long-range spatiotemporal dependencies capturing plays an essential role in improving video features for action recognition. The non-local block inspired by the non-local means is…
ARID: A New Dataset for Recognizing Action in the Dark
Yuecong Xu, Jianfei Yang, Haozhi Cao +3
The task of action recognition in dark videos is useful in various scenarios, e.g., night surveillance and self-driving at night. Though progress has been made in the action recogn…
Exploiting Inter-Frame Regional Correlation for Efficient Action Recognition
Yuecong Xu, Jianfei Yang, Kezhi Mao +2
Temporal feature extraction is an important issue in video-based action recognition. Optical flow is a popular method to extract temporal feature, which produces excellent performa…
Improving Relation Extraction with Knowledge-attention
Pengfei Li, Kezhi Mao, Xuefeng Yang +1
While attention mechanisms have been proven to be effective in many NLP tasks, majority of them are data-driven. We propose a novel knowledge-attention encoder which incorporates p…
Deep Learning and Its Applications to Machine Health Monitoring: A Survey
Rui Zhao, Ruqiang Yan, Zhenghua Chen +3
Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image…
Supervised Fine Tuning for Word Embedding with Integrated Knowledge
Xuefeng Yang, Kezhi Mao
Learning vector representation for words is an important research field which may benefit many natural language processing tasks. Two limitations exist in nearly all available mode…