223 citations · 354 across the 31 of their papers we have counts for
7 papers · 1 filter
Collaborative Generative Hashing for Marketing and Fast Cold-start Recommendation
Yan Zhang, Ivor W. Tsang, Lixin Duan
Cold-start has being a critical issue in recommender systems with the explosion of data in e-commerce. Most existing studies proposed to alleviate the cold-start problem are also k…
Region Comparison Network for Interpretable Few-shot Image Classification
Zhiyu Xue, Lixin Duan, Wen Li +2
While deep learning has been successfully applied to many real-world computer vision tasks, training robust classifiers usually requires a large amount of well-labeled data. Howeve…
Dynamic and Static Context-aware LSTM for Multi-agent Motion Prediction
Chaofan Tao, Qinhong Jiang, Lixin Duan +1
Multi-agent motion prediction is challenging because it aims to foresee the future trajectories of multiple agents (\textit{e.g.} pedestrians) simultaneously in a complicated scene…
Reconstruction Regularized Deep Metric Learning for Multi-label Image Classification
Changsheng Li, Chong Liu, Lixin Duan +2
In this paper, we present a novel deep metric learning method to tackle the multi-label image classification problem. In order to better learn the correlations among images feature…
Deeply Aligned Adaptation for Cross-domain Object Detection
Minghao Fu, Zhenshan Xie, Wen Li +1
Cross-domain object detection has recently attracted more and more attention for real-world applications, since it helps build robust detectors adapting well to new environments. I…
Learning Cross-domain Semantic-Visual Relationships for Transductive Zero-Shot Learning
Fengmao Lv, Jianyang Zhang, Guowu Yang +3
Zero-Shot Learning (ZSL) learns models for recognizing new classes. One of the main challenges in ZSL is the domain discrepancy caused by the category inconsistency between trainin…
Unbiased Mean Teacher for Cross-domain Object Detection
Jinhong Deng, Wen Li, Yuhua Chen +1
Cross-domain object detection is challenging, because object detection model is often vulnerable to data variance, especially to the considerable domain shift between two distincti…