Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Song, Lingxiao"'
Autor:
Song, Lingxiao, Zhang, Tingyuan, Zhang, Xuan, Tian, Jinfeng, Wang, Jiajia, Yang, Jiahui, Wang, Wei, Lin, Keying, Feng, Dong, Ma, Baojun
Publikováno v:
In Applied Catalysis B: Environment and Energy 15 December 2024 359
Autor:
Song, Lingxiao, Yong, Xuechao, Zhang, Peilei, Song, Shijie, Chen, Kefan, Yan, Hua, Sun, Tianzhu, Lu, Qinghua, Shi, Haichuan, Chen, Yu, Huang, Yuze
Publikováno v:
In Optics and Laser Technology February 2025 181 Part A
Autor:
Lin, Keying, Ma, Xiaolian, Song, Lingxiao, Tian, Jinfeng, Zhan, Haijuan, Wang, Wei, Gao, Xinhua, Ma, Baojun
Publikováno v:
In Chemical Engineering Science 5 December 2023 282
Akademický článek
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Akademický článek
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Near infrared-visible (NIR-VIS) heterogeneous face recognition refers to the process of matching NIR to VIS face images. Current heterogeneous methods try to extend VIS face recognition methods to the NIR spectrum by synthesizing VIS images from NIR
Externí odkaz:
http://arxiv.org/abs/1902.03565
A capsule is a group of neurons whose activity vector models different properties of the same entity. This paper extends the capsule to a generative version, named variational capsules (VCs). Each VC produces a latent variable for a specific entity,
Externí odkaz:
http://arxiv.org/abs/1807.04099
Facial expression synthesis has drawn much attention in the field of computer graphics and pattern recognition. It has been widely used in face animation and recognition. However, it is still challenging due to the high-level semantic presence of lar
Externí odkaz:
http://arxiv.org/abs/1712.03474
Occluded face detection is a challenging detection task due to the large appearance variations incurred by various real-world occlusions. This paper introduces an Adversarial Occlusion-aware Face Detector (AOFD) by simultaneously detecting occluded f
Externí odkaz:
http://arxiv.org/abs/1709.05188
The gap between sensing patterns of different face modalities remains a challenging problem in heterogeneous face recognition (HFR). This paper proposes an adversarial discriminative feature learning framework to close the sensing gap via adversarial
Externí odkaz:
http://arxiv.org/abs/1709.03675