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1,018 results on '"Zheng-Wei Shi"'

211. Correlation Based Identity Filter: An Efficient Framework for Person Search

Author

Li, Wei-Hong, Mao, Yafang, Wu, Ancong, Zheng, Wei-Shi, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Zhao, Yao, editor, Kong, Xiangwei, editor, and Taubman, David, editor

Published
2017
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212. One-Snapshot Face Anti-spoofing Using a Light Field Camera

Author

Xie, Xiaohua, Gao, Yan, Zheng, Wei-Shi, Lai, Jianhuang, Zhu, Junyong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Zhou, Jie, editor, Wang, Yunhong, editor, Sun, Zhenan, editor, Xu, Yong, editor, Shen, Linlin, editor, Feng, Jianjiang, editor, Shan, Shiguang, editor, Qiao, Yu, editor, Guo, Zhenhua, editor, and Yu, Shiqi, editor

Published
2017
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222. Real-Time RGB-D Activity Prediction by Soft Regression

Author

Hu, Jian-Fang, Zheng, Wei-Shi, Ma, Lianyang, Wang, Gang, Lai, Jianhuang, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Leibe, Bastian, editor, Matas, Jiri, editor, Sebe, Nicu, editor, and Welling, Max, editor

Published
2016
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232. Active Shape Model Based on Sparse Representation

Author

Guo, Yanqing, He, Ran, Zheng, Wei-Shi, Kong, Xiangwei, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Zheng, Wei-Shi, editor, Sun, Zhenan, editor, Wang, Yunhong, editor, Chen, Xilin, editor, Yuen, Pong C., editor, and Lai, Jianhuang, editor

Published
2012
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233. Complete Gradient Face: A Novel Illumination Invariant Descriptor

Author

Zhu, Jun-Yong, Zheng, Wei-Shi, Lai, Jian-Huang, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Zheng, Wei-Shi, editor, Sun, Zhenan, editor, Wang, Yunhong, editor, Chen, Xilin, editor, Yuen, Pong C., editor, and Lai, Jianhuang, editor

Published
2012
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234. A Survey of Face Hallucination

Author

Liang, Yan, Lai, Jian-Huang, Zheng, Wei-Shi, Cai, Zemin, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Zheng, Wei-Shi, editor, Sun, Zhenan, editor, Wang, Yunhong, editor, Chen, Xilin, editor, Yuen, Pong C., editor, and Lai, Jianhuang, editor

Published
2012
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235. Class attention to regions of lesion for imbalanced medical image recognition

Author

Zhuang, Jiaxin, Cai, Jiabin, Zhang, Jianguo, Zheng, Wei-shi, Wang, Ruixuan, Zhuang, Jiaxin, Cai, Jiabin, Zhang, Jianguo, Zheng, Wei-shi, and Wang, Ruixuan

Abstract

Automated medical image classification is the key component in intelligent diagnosis systems. However, most medical image datasets contain plenty of samples of common diseases and just a handful of rare ones, leading to major class imbalances. Currently, it is an open problem in intelligent diagnosis to effectively learn from imbalanced training data. In this paper, we propose a simple yet effective framework, named Class Attention to REgions of the lesion (CARE), to handle data imbalance issues by embedding attention into the training process of Convolutional Neural Networks (CNNs). The proposed attention module helps CNNs attend to lesion regions of rare diseases, therefore helping CNNs to learn their characteristics more effectively. In addition, this attention module works only during the training phase and does not change the architecture of the original network, so it can be directly combined with any existing CNN architecture. The CARE framework needs bounding boxes to represent the lesion regions of rare diseases. To alleviate the need for manual annotation, we further developed variants of CARE by leveraging the traditional saliency methods or a pretrained segmentation model for bounding box generation. Results show that the CARE variants with automated bounding box generation are comparable to the original CARE framework with manual bounding box annotations. A series of experiments on an imbalanced skin image dataset and a pneumonia dataset indicates that our method can effectively help the network focus on the lesion regions of rare diseases and remarkably improves the classification performance of rare diseases. © 2023 Elsevier B.V.

Published
2023

240. One-Shot Learning of Sketch Categories with Co-regularized Sparse Coding

Author

Qi, Yonggang, Zheng, Wei-Shi, Xiang, Tao, Song, Yi-Zhe, Zhang, Honggang, Guo, Jun, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bebis, George, editor, Boyle, Richard, editor, Parvin, Bahram, editor, Koracin, Darko, editor, McMahan, Ryan, editor, Jerald, Jason, editor, Zhang, Hui, editor, Drucker, Steven M., editor, Kambhamettu, Chandra, editor, El Choubassi, Maha, editor, Deng, Zhigang, editor, and Carlson, Mark, editor

Published
2014
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241. Person Re-identification with Data-Driven Features

Author

Li, Xiang, Gao, Jinyu, Chang, Xiaobin, Mai, Yuting, Zheng, Wei-Shi, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Sun, Zhenan, editor, Shan, Shiguang, editor, Sang, Haifeng, editor, Zhou, Jie, editor, Wang, Yunhong, editor, and Yuan, Weiqi, editor

Published
2014
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242. A Similar Interaction Model for Group Activity Recognition in Still Images

Author

Chang, Xiaobin, Li, Xiang, Mai, Yuting, Zheng, Wei-Shi, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Sun, Zhenan, editor, Shan, Shiguang, editor, Sang, Haifeng, editor, Zhou, Jie, editor, Wang, Yunhong, editor, and Yuan, Weiqi, editor

Published
2014
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243. The Methods of Modeling the Image Sets Based on MEAP and Its Application on Face Recognition

Author

Wang, Qian, Lai, Jian-huang, Liu, Na, Zheng, Wei-Shi, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Sun, Zhenan, editor, Shan, Shiguang, editor, Sang, Haifeng, editor, Zhou, Jie, editor, Wang, Yunhong, editor, and Yuan, Weiqi, editor

Published
2014
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244. Revisiting Person Re-Identification by Camera Selection

Author

Peng, Yi-Xing, Li, Yuanxun, and Zheng, Wei-Shi

Abstract

Person re-identification (Re-ID) is a fundamental task in visual surveillance. Given a query image of the target person, conventional Re-ID focuses on the pairwise similarities between the candidate images and the query. However, conventional Re-ID does not evaluate the consistency of the retrieval results of whether the most similar images ranked in each place contain the same person, which is risky in some applications such as missing out a place where the patient passed will hinder the epidemiological investigation. In this work, we investigate a more challenging task: consistently and successfully retrieving the target person in all camera views. We define the task as continuous person Re-ID and propose a corresponding evaluation metric termed overall Rank-K accuracy. Different from the conventional Re-ID, any incorrect retrieval under an individual camera view that raises an inconsistency will fail the continuous Re-ID. Consequently, the defective cameras, in which the images are hard to be automatically associated with the images from other views, strongly degrade the performance of continuous person Re-ID. Since the camera deployment is crucial for continuous tracking across camera views, we rethink person Re-ID from the perspective of camera deployment and assess the quality of a camera network by performing continuous Re-ID. Moreover, we propose to automatically detect the defective cameras that greatly hamper the continuous Re-ID. Because brute-force search is costly when the camera network becomes complicated, we explicitly model the visual relations as well as the spatial relations among cameras and develop a relational deep Q-network to select the properly deployed cameras and the un-selected cameras are regarded as the defective cameras. Since most existing datasets do not provide topology information about the camera network, they are unsuitable for investigating the importance of spatial relations on camera selection. Thus, we collect a new dataset including 20 cameras with topology information. Compared with randomly removing cameras, the experimental results show that our method can effectively detect the defective cameras so that people could take further operations on these cameras in practice (https://www.isee-ai.cn/∼yixing/MCCPD.html).

Published
2024
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245. Generalized Intra-Camera Supervised Person Re-Identification

Author

Peng, Yi-Xing, Tang, Yu-Ming, Lin, Kun-Yu, and Zheng, Wei-Shi

Abstract

Person re-identification (Re-ID) is to match the images of the same person from different camera views, which demands a view-invariant feature embedding. Recently, intra-camera supervised (ICS) Re-ID develops the Re-ID models without cross-view annotated data. Existing ICS methods are developed based on the assumptions, such as assuming each person in the training set appears under multiple cameras. However, there is no guarantee that the assumptions are true without cross-view annotations, and their performance degrades when the assumptions are violated. In this work, we generalize the ICS Re-ID and develop an ICS Re-ID model without the assumptions. The absence of prior assumptions and cross-view annotations poses a challenge in exploiting the discriminative information among cross-view images. To this end, we propose to mine the view-invariant relations between cross-view images for Re-ID model to exploit discriminative information and overcome the cross-view variations. Specifically, we learn composited view-aware features by compositing the identity information with different camera view information in the feature composition module. Then, we exploit the composited features to model various view-aware relations between pairwise images. By mining the common patterns among the view-aware relations, we obtain the view-invariant pairwise relation for learning. Besides, leveraging the composited view-aware features, we develop a view-aware marginal constraint for robust cross-view learning. To facilitate learning the feature composition module, we augment an auxiliary network to exploit the camera view information at the feature level. Extensive experimental results show the effectiveness of our method under different scenarios.

Published
2024
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