114 citations · 207 across the 11 of their papers we have counts for
12 papers
Discriminative-Generative Dual Memory Video Anomaly Detection
Xin Guo, Zhongming Jin, Chong Chen +5
Recently, people tried to use a few anomalies for video anomaly detection (VAD) instead of only normal data during the training process. A side effect of data imbalance occurs when…
PCPL: Predicate-Correlation Perception Learning for Unbiased Scene Graph Generation
Shaotian Yan, Chen Shen, Zhongming Jin +4
Today, scene graph generation(SGG) task is largely limited in realistic scenarios, mainly due to the extremely long-tailed bias of predicate annotation distribution. Thus, tackling…
Deep Robust Clustering by Contrastive Learning
Huasong Zhong, Chong Chen, Zhongming Jin +1
Recently, many unsupervised deep learning methods have been proposed to learn clustering with unlabelled data. By introducing data augmentation, most of the latest methods look int…
Apparel-invariant Feature Learning for Apparel-changed Person Re-identification
Zhengxu Yu, Yilun Zhao, Bin Hong +5
With the rise of deep learning methods, person Re-Identification (ReID) performance has been improved tremendously in many public datasets. However, most public ReID datasets are c…
Out-of-distribution Generalization via Partial Feature Decorrelation
Xin Guo, Zhengxu Yu, Chao Xiang +5
Most deep-learning-based image classification methods assume that all samples are generated under an independent and identically distributed (IID) setting. However, out-of-distribu…
Adversarial Mutual Information for Text Generation
Boyuan Pan, Yazheng Yang, Kaizhao Liang +5
Recent advances in maximizing mutual information (MI) between the source and target have demonstrated its effectiveness in text generation. However, previous works paid little atte…
A Fast Sampling Gradient Tree Boosting Framework
Daniel Chao Zhou, Zhongming Jin, Tong Zhang
As an adaptive, interpretable, robust, and accurate meta-algorithm for arbitrary differentiable loss functions, gradient tree boosting is one of the most popular machine learning t…
Progressive Transfer Learning
Zhengxu Yu, Dong Shen, Zhongming Jin +3
Model fine-tuning is a widely used transfer learning approach in person Re-identification (ReID) applications, which fine-tuning a pre-trained feature extraction model into the tar…
Deep Active Learning for Video-based Person Re-identification
Menglin Wang, Baisheng Lai, Zhongming Jin +3
It is prohibitively expensive to annotate a large-scale video-based person re-identification (re-ID) dataset, which makes fully supervised methods inapplicable to real-world deploy…
Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction
Ken Chen, Fei Chen, Baisheng Lai +8
Forecasting future traffic flows from previous ones is a challenging problem because of their complex and dynamic nature of spatio-temporal structures. Most existing graph-based CN…
Sharp Attention Network via Adaptive Sampling for Person Re-identification
Chen Shen, Guo-Jun Qi, Rongxin Jiang +4
In this paper, we present novel sharp attention networks by adaptively sampling feature maps from convolutional neural networks (CNNs) for person re-identification (re-ID) problem.…
Density Sensitive Hashing
Yue Lin, Deng Cai, Cheng Li
Nearest neighbors search is a fundamental problem in various research fields like machine learning, data mining and pattern recognition. Recently, hashing-based approaches, e.g., L…