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Wen Gao
Researcher at Peking University
Publications - 1373
Citations - 41199
Wen Gao is an academic researcher from Peking University. The author has contributed to research in topics: Motion compensation & Coding tree unit. The author has an hindex of 88, co-authored 1336 publications receiving 36100 citations. Previous affiliations of Wen Gao include Tencent & Shanghai University of Electric Power.
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Proceedings ArticleDOI
Person Transfer GAN to Bridge Domain Gap for Person Re-identification
TL;DR: A Person Transfer Generative Adversarial Network (PTGAN) is proposed to relieve the expensive costs of annotating new training samples and comprehensive experiments show that the domain gap could be substantially narrowed-down by the PTGAN.
Proceedings ArticleDOI
Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition
TL;DR: A novel non-statistics based face representation approach, local Gabor binary pattern histogram sequence (LGBPHS), in which training procedure is unnecessary to construct the face model, so that the generalizability problem is naturally avoided.
Journal ArticleDOI
WLD: A Robust Local Image Descriptor
TL;DR: Experimental results on the Brodatz and KTH-TIPS2-a texture databases show that WLD impressively outperforms the other widely used descriptors (e.g., Gabor and SIFT), and experimental results on human face detection also show a promising performance comparable to the best known results onThe MIT+CMU frontal face test set, the AR face data set, and the CMU profile test set.
Journal ArticleDOI
The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations
TL;DR: The evaluation protocol based on the CAS-PEAL-R1 database is discussed and the performance of four algorithms are presented as a baseline to do the following: elementarily assess the difficulty of the database for face recognition algorithms; preference evaluation results for researchers using the database; and identify the strengths and weaknesses of the commonly used algorithms.
Proceedings ArticleDOI
Pose-Driven Deep Convolutional Model for Person Re-identification
TL;DR: Zhang et al. as mentioned in this paper proposed a Pose-driven Deep Convolutional (PDC) model to learn improved feature extraction and matching models from end to end, which explicitly leverages the human part cues to alleviate the pose variations and learn robust feature representations from both the global image and different local parts.