Zobrazeno 1 - 10
of 405
pro vyhledávání: '"Li, Jilin"'
Autor:
Li, Jian, Zhang, Bin, Wang, Yabiao, Tai, Ying, Zhang, ZhenYu, Wang, Chengjie, Li, Jilin, Huang, Xiaoming, Xia, Yili
Along with current multi-scale based detectors, Feature Aggregation and Enhancement (FAE) modules have shown superior performance gains for cutting-edge object detection. However, these hand-crafted FAE modules show inconsistent improvements on face
Externí odkaz:
http://arxiv.org/abs/2201.10781
Publikováno v:
In Heliyon 30 July 2024 10(14)
Autor:
Li, Yuxi, Zhang, Boshen, Li, Jian, Wang, Yabiao, Lin, Weiyao, Wang, Chengjie, Li, Jilin, Huang, Feiyue
In this paper, we place the atomic action detection problem into a Long-Short Term Context (LSTC) to analyze how the temporal reliance among video signals affect the action detection results. To do this, we decompose the action recognition pipeline i
Externí odkaz:
http://arxiv.org/abs/2110.09819
The rapid development of facial manipulation techniques has aroused public concerns in recent years. Following the success of deep learning, existing methods always formulate DeepFake video detection as a binary classification problem and develop fra
Externí odkaz:
http://arxiv.org/abs/2109.01860
Autor:
Liu, Shubao, Zhang, Ke-Yue, Yao, Taiping, Bi, Mingwei, Ding, Shouhong, Li, Jilin, Huang, Feiyue, Ma, Lizhuang
With various face presentation attacks arising under unseen scenarios, face anti-spoofing (FAS) based on domain generalization (DG) has drawn growing attention due to its robustness. Most existing methods utilize DG frameworks to align the features t
Externí odkaz:
http://arxiv.org/abs/2108.02667
Autor:
Song, Qingyu, Wang, Changan, Jiang, Zhengkai, Wang, Yabiao, Tai, Ying, Wang, Chengjie, Li, Jilin, Huang, Feiyue, Wu, Yang
Localizing individuals in crowds is more in accordance with the practical demands of subsequent high-level crowd analysis tasks than simply counting. However, existing localization based methods relying on intermediate representations (\textit{i.e.},
Externí odkaz:
http://arxiv.org/abs/2107.12746
Autor:
Wang, Changan, Song, Qingyu, Zhang, Boshen, Wang, Yabiao, Tai, Ying, Hu, Xuyi, Wang, Chengjie, Li, Jilin, Ma, Jiayi, Wu, Yang
Recently, the problem of inaccurate learning targets in crowd counting draws increasing attention. Inspired by a few pioneering work, we solve this problem by trying to predict the indices of pre-defined interval bins of counts instead of the count v
Externí odkaz:
http://arxiv.org/abs/2107.12619
Autor:
Zhang, Ke-Yue, Yao, Taiping, Zhang, Jian, Liu, Shice, Yin, Bangjie, Ding, Shouhong, Li, Jilin
In pursuit of consolidating the face verification systems, prior face anti-spoofing studies excavate the hidden cues in original images to discriminate real persons and diverse attack types with the assistance of auxiliary supervision. However, limit
Externí odkaz:
http://arxiv.org/abs/2107.10628
Autor:
Liu, Shubao, Zhang, Ke-Yue, Yao, Taiping, Sheng, Kekai, Ding, Shouhong, Tai, Ying, Li, Jilin, Xie, Yuan, Ma, Lizhuang
Face anti-spoofing approaches based on domain generalization (DG) have drawn growing attention due to their robustness for unseen scenarios. Previous methods treat each sample from multiple domains indiscriminately during the training process, and en
Externí odkaz:
http://arxiv.org/abs/2106.16128
Autor:
Wang, Yuhan, Chen, Xu, Zhu, Junwei, Chu, Wenqing, Tai, Ying, Wang, Chengjie, Li, Jilin, Wu, Yongjian, Huang, Feiyue, Ji, Rongrong
In this work, we propose a high fidelity face swapping method, called HifiFace, which can well preserve the face shape of the source face and generate photo-realistic results. Unlike other existing face swapping works that only use face recognition m
Externí odkaz:
http://arxiv.org/abs/2106.09965