Zobrazeno 1 - 10
of 457
pro vyhledávání: '"Yan Zhiyuan"'
Autor:
MA Xiaoming, LI Ruiping, YAN Zhiyuan, LI Xinlei, LI Zhengzhong, PAN Hongmei, WANG Xiangdong, LI Yumin
Publikováno v:
Guan'gai paishui xuebao, Vol 41, Iss 11, Pp 6-13 (2022)
【Objective】 Hetao irrigation district is one of the largest surface water-irrigation districts in north China. Keeping groundwater table below a critical depth is essential to preventing salt accumulation in the root zone, and the purpose of this
Externí odkaz:
https://doaj.org/article/4e975fd77d154b92bda15a759edc1db8
Three key challenges hinder the development of current deepfake video detection: (1) Temporal features can be complex and diverse: how can we identify general temporal artifacts to enhance model generalization? (2) Spatiotemporal models often lean he
Externí odkaz:
http://arxiv.org/abs/2408.17065
The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed "blendfake", encouraging model
Externí odkaz:
http://arxiv.org/abs/2408.17052
Learning intrinsic bias from limited data has been considered the main reason for the failure of deepfake detection with generalizability. Apart from the discovered content and specific-forgery bias, we reveal a novel spatial bias, where detectors in
Externí odkaz:
http://arxiv.org/abs/2408.06779
Autor:
Yan, Zhiyuan, Yao, Taiping, Chen, Shen, Zhao, Yandan, Fu, Xinghe, Zhu, Junwei, Luo, Donghao, Yuan, Li, Wang, Chengjie, Ding, Shouhong, Wu, Yunsheng
We propose a new comprehensive benchmark to revolutionize the current deepfake detection field to the next generation. Predominantly, existing works identify top-notch detection algorithms and models by adhering to the common practice: training detec
Externí odkaz:
http://arxiv.org/abs/2406.13495
Autor:
Jia, Shan, Lyu, Reilin, Zhao, Kangran, Chen, Yize, Yan, Zhiyuan, Ju, Yan, Hu, Chuanbo, Li, Xin, Wu, Baoyuan, Lyu, Siwei
DeepFakes, which refer to AI-generated media content, have become an increasing concern due to their use as a means for disinformation. Detecting DeepFakes is currently solved with programmed machine learning algorithms. In this work, we investigate
Externí odkaz:
http://arxiv.org/abs/2403.14077
Assertion-based verification (ABV) is a critical method for ensuring design circuits comply with their architectural specifications, which are typically described in natural language. This process often requires significant interpretation by engineer
Externí odkaz:
http://arxiv.org/abs/2402.00386
Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection
Deepfake detection faces a critical generalization hurdle, with performance deteriorating when there is a mismatch between the distributions of training and testing data. A broadly received explanation is the tendency of these detectors to be overfit
Externí odkaz:
http://arxiv.org/abs/2311.11278
Autor:
Bi, Hanbo, Feng, Yingchao, Yan, Zhiyuan, Mao, Yongqiang, Diao, Wenhui, Wang, Hongqi, Sun, Xian
Few-shot segmentation (FSS) is proposed to segment unknown class targets with just a few annotated samples. Most current FSS methods follow the paradigm of mining the semantics from the support images to guide the query image segmentation. However, s
Externí odkaz:
http://arxiv.org/abs/2310.12452
Autor:
Li, Xingquan, Tao, Simin, Huang, Zengrong, Chen, Shijian, Zeng, Zhisheng, Ni, Liwei, Huang, Zhipeng, Zhuang, Chunan, Wu, Hongxi, Li1, Weiguo, Zhao, Xueyan, Liu, He, Long, Shuaiying, He, Wei, Liu, Bojun, Gan, Sifeng, Yu, Zihao, Liu, Tong, Miao, Yuchi, Yan, Zhiyuan, Wang, Hao, Zhao, Jie, Li, Yifan, Liu, Ruizhi, Lin, Xiaoze, Yang, Bo, Xue, Zhen, Huang, Fuxing, Yang, Zonglin, Wu, Zhenggang, Li, Jiangkao, Liu, Yuezuo, Peng, Ming, Qiu, Yihang, Wu, Wenrui, Shao, Zheqing, Mo, Kai, Liu, Jikang, Liang, Yuyao, Zhang, Mingzhe, Ma, Zhuang, Cong, Xiang, Huang, Daxiang, Luo, Guojie, Li, Huawei, Shen, Haihua, Chen, Mingyu, Bu, Dongbo, Zhu, Wenxing, Cai, Ye, Xiong, Xiaoming, Jiang, Ying, Heng, Yi, Zhang, Peng, Xie, Biwei, Bao, Yungang
Open-source EDA shows promising potential in unleashing EDA innovation and lowering the cost of chip design. This paper presents an open-source EDA project, iEDA, aiming for building a basic infrastructure for EDA technology evolution and closing the
Externí odkaz:
http://arxiv.org/abs/2308.01857