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您的检索式：作者名="Bineng ZHONG"
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| 1 | Background subtraction driven seeds selection for moving objects segmentation and matting显示文摘 | Zhong Bineng Chen Yan | 2013 | Neurocomputing2013，103，： | 1 |
| 2 | DRNet:Towards fast,accurate and practical dish recognition显示文摘Existing algorithms of dish recognition mainly focus on accuracy with predefined classes,thus limiting their application scope.In this paper,we propose a practical two-stage dish recognition framework(DRNet)that yields a tradeoff between speed and accuracy while adapting to the variation in class numbers.In the first stage,we build an arbitrary-oriented dish detector(AODD)to localize dish position,which can effectively alleviate the impact of background noise and pose variations.In the second stage,we propose a dish reidentifier(DReID)to recognize the registered dishes to handle uncertain categories.To further improve the accuracy of DRNet,we design an attribute recognition(AR)module to predict the attributes of dishes.The attributes are used as auxiliary information to enhance the discriminative ability of DRNet.Moreover,pruning and quantization are processed on our model to be deployed in embedded environments.Finally,to facilitate the study of dish recognition,a well-annotated dataset is established.Our AODD,DReID,AR,and DRNet run at about 14,25,16,and 5 fps on the hardware RKNN 3399 pro,respectively. | CHENG SiYuan CHU BinFei ZHONG BiNeng ZHANG ZiKai LIU Xin TANG ZhenJun LI XianXian | 2021 | Science China(Technological Sciences)2021，64，12： | 1 |
| 3 | Robust feature learning for online discriminative tracking without large-scale pre-training显示文摘Owing to the inherent lack of training data in visual tracking,recent work in deep learning-based trackers has focused on learning a generic representation ofltine from large-scale training data and transferring the pre-trained feature representation to a tracking task.Offline pre-training is time-consuming,and the learned generic representation may be either less discriminative for tracking specific objects or overfitted to typical tracking datasets.In this paper, we propose an online discriminative tracking method based on robust feature learning without large-scale pre-training. Specifically,we first design a PCA filter bank-based convolutional neural network (CNN)architecture to learn robust features online with a few positive and negative samples in the high-dimensional feature space.Then,we use a simple softthresholding method to produce sparse features that are more robust to target appearance variations.Moreover,we increase the reliability of our tracker using edge information generated from edge box proposals during the process of visual tracking.Finally,effective visual tracking results are achieved by systematically combining the tracking information and edge box-based scores in a particle filtering framework.Extensive results on the widely used online tracking benchmark (OTB-50)with 50videos validate the robustness and effectiveness of the proposed tracker without large-scale pre-training. | Jun ZHANG Bineng ZHONG Pengfei WANG Cheng WANG Jixiang DU | 2018 | Frontiers of Computer Science2018，12，6： | 1 |
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