Zobrazeno 1 - 10
of 103
pro vyhledávání: '"Zhen, Liangli"'
Autor:
Nguyen, Khoi Nguyen Tiet, Zhang, Wenyu, Lu, Kangkang, Wu, Yuhuan, Zheng, Xingjian, Tan, Hui Li, Zhen, Liangli
Deep learning models excel in various computer vision tasks but are susceptible to adversarial examples-subtle perturbations in input data that lead to incorrect predictions. This vulnerability poses significant risks in safety-critical applications
Externí odkaz:
http://arxiv.org/abs/2408.01934
Autor:
Wu, Yu-Huan, Zhang, Shi-Chen, Liu, Yun, Zhang, Le, Zhan, Xin, Zhou, Daquan, Feng, Jiashi, Cheng, Ming-Ming, Zhen, Liangli
Semantic segmentation tasks naturally require high-resolution information for pixel-wise segmentation and global context information for class prediction. While existing vision transformers demonstrate promising performance, they often utilize high r
Externí odkaz:
http://arxiv.org/abs/2310.05026
Autor:
Du, Jiawei, Yan, Hanshu, Feng, Jiashi, Zhou, Joey Tianyi, Zhen, Liangli, Goh, Rick Siow Mong, Tan, Vincent Y. F.
Overparametrized Deep Neural Networks (DNNs) often achieve astounding performances, but may potentially result in severe generalization error. Recently, the relation between the sharpness of the loss landscape and the generalization error has been es
Externí odkaz:
http://arxiv.org/abs/2110.03141
Autor:
Liu, Ping, Lin, Yuewei, He, Yang, Wei, Yunchao, Zhen, Liangli, Zhou, Joey Tianyi, Goh, Rick Siow Mong, Liu, Jingen
In this paper, we propose to utilize Automated Machine Learning to adaptively search a neural architecture for deepfake detection. This is the first time to employ automated machine learning for deepfake detection. Based on our explored search space,
Externí odkaz:
http://arxiv.org/abs/2106.10705
Given a video, video grounding aims to retrieve a temporal moment that semantically corresponds to a language query. In this work, we propose a Parallel Attention Network with Sequence matching (SeqPAN) to address the challenges in this task: multi-m
Externí odkaz:
http://arxiv.org/abs/2105.08481
Autor:
Zhang, Hao, Sun, Aixin, Jing, Wei, Nan, Guoshun, Zhen, Liangli, Zhou, Joey Tianyi, Goh, Rick Siow Mong
Given a collection of untrimmed and unsegmented videos, video corpus moment retrieval (VCMR) is to retrieve a temporal moment (i.e., a fraction of a video) that semantically corresponds to a given text query. As video and text are from two distinct f
Externí odkaz:
http://arxiv.org/abs/2105.06247
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Natural Language Video Localization (NLVL) aims to locate a target moment from an untrimmed video that semantically corresponds to a text query. Existing approaches mainly solve the NLVL problem from the perspective of computer vision by formulating
Externí odkaz:
http://arxiv.org/abs/2102.13558
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Feng, Yangqin, Wang, Zizhou, Xu, Xinxing, Wang, Yan, Fu, Huazhu, Li, Shaohua, Zhen, Liangli, Lei, Xiaofeng, Cui, Yingnan, Sim Zheng Ting, Jordan, Ting, Yonghan, Zhou, Joey Tianyi, Liu, Yong, Siow Mong Goh, Rick, Heng Tan, Cher
Publikováno v:
In Medical Image Analysis January 2023 83
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.