Publications

@article{Zhang2020OrderlessReID,

title = {Ordered or Orderless: A Revisit for Video based Person Re-Identification},

author = {Le Zhang and Zenglin Shi and Joey Tianyi Zhou and Ming-Ming Cheng and Yun Liu and Jia-Wang Bian and Zeng Zeng and Chunhua Shen},

url = {https://github.com/ZhangLeUestc/VideoReid-TPAMI2020},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {IEEE TPAMI},

abstract = {Is recurrent network really necessary for learning a good visual representation for video based person re-identification (VPRe-id)? In this paper, we first show that the common practice of employing recurrent neural networks (RNNs) to aggregate temporal spatial features may not be optimal. Specifically, with a diagnostic analysis, we show that the recurrent structure may not be effective to learn temporal dependencies than what we expected and implicitly yields an orderless representation. Based on this observation, we then present a simple yet surprisingly powerful approach for VPRe-id, where we treat VPRe-id as an efficient orderless ensemble of image based person re-identification problem. More specifically, we divide videos into individual images and re-identify person with ensemble of image based rankers. Under the i.i.d. assumption, we provide an error bound that sheds light upon how could we improve VPRe-id. Our work also presents a promising way to bridge the gap between video and image based person re-identification. Comprehensive experimental evaluations demonstrate that the proposed solution achieves state-of-the-art performances on multiple widely used datasets (iLIDS-VID, PRID 2011, and MARS).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zhang2019persemon,

title = {PersEmoN: a deep network for joint analysis of apparent personality, emotion and their relationship},

author = {Le Zhang and Songyou Peng and Stefan Winkler},

year  = {2019},

date = {2019-01-01},

journal = {IEEE Transactions on Affective Computing},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zhou2019attention,

title = {Attention-driven loss for anomaly detection in video surveillance},

author = {Joey Tianyi Zhou and Le Zhang and Zhiwen Fang and Jiawei Du and Xi Peng and Yang Xiao},

url = {https://github.com/joeyzhouty/Attention-driven-loss},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {IEEE TCSVT},

volume = {30},

number = {12},

pages = {4639--4647},

publisher = {IEEE},

abstract = {Recent video anomaly detection methods focus on reconstructing or predicting frames. Under this umbrella, the long-standing inter-class data-imbalance problem resorts to the imbalance between foreground and stationary background objects in video anomaly detection and this has been less investigated by existing solutions. Naively optimizing the reconstructing loss yields a biased optimization towards background reconstruction rather than the objects of interest in the foreground. To solve this, we proposed a simple yet effective solution, termed attention-driven loss to alleviate the foreground-background imbalance problem in anomaly detection. Specifically, we compute a single mask map that summarizes the frame evolution of moving foreground regions and suppresses the background in the training video clips. After that, we construct an attention map through the combination of the mask map and background to give different weights to the foreground and background region respectively. The proposed attention-driven loss is independent of backbone networks and can be easily augmented in most existing anomaly detection models. Augmented with attention-driven loss, the model is able to achieve AUC 86.0% on Avenue, 83.9% on Ped1, 96% on Ped2 datasets. Extensive experimental results and ablation studies further validate the effectiveness of our model. Project page is available at https://github.com/joeyzhouty/Attention-driven-loss},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{chen2018wifi,

title = {WiFi CSI based passive human activity recognition using attention based BLSTM},

author = {Zhenghua Chen and Le Zhang and Chaoyang Jiang and Zhiguang Cao and Wei Cui},

year  = {2018},

date = {2018-01-01},

journal = {IEEE Transactions on Mobile Computing},

volume = {18},

number = {11},

pages = {2714--2724},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zhang2017benchmarking,

title = {Benchmarking ensemble classifiers with novel co-trained kernel ridge regression and random vector functional link ensembles [research frontier]},

author = {Le Zhang and Ponnuthurai Nagaratnam Suganthan},

year  = {2017},

date = {2017-01-01},

journal = {IEEE Computational Intelligence Magazine},

volume = {12},

number = {4},

pages = {61--72},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}