Zobrazeno 1 - 10
of 232
pro vyhledávání: '"Wu, Tianxing"'
Autor:
Wang, Yuxiang, Ye, Shuzhan, Xu, Xiaoliang, Geng, Yuxia, Zhao, Zhenghe, Ke, Xiangyu, Wu, Tianxing
Given an attributed graph $G$ and a query node $q$, \underline{C}ommunity \underline{S}earch over \underline{A}ttributed \underline{G}raphs (CS-AG) aims to find a structure- and attribute-cohesive subgraph from $G$ that contains $q$. Although CS-AG h
Externí odkaz:
http://arxiv.org/abs/2402.17242
Fault localization is challenging in online micro-service due to the wide variety of monitoring data volume, types, events and complex interdependencies in service and components. Faults events in services are propagative and can trigger a cascade of
Externí odkaz:
http://arxiv.org/abs/2402.13264
Ontologies contain rich knowledge within domain, which can be divided into two categories, namely extensional knowledge and intensional knowledge. Extensional knowledge provides information about the concrete instances that belong to specific concept
Externí odkaz:
http://arxiv.org/abs/2402.01677
Though diffusion-based video generation has witnessed rapid progress, the inference results of existing models still exhibit unsatisfactory temporal consistency and unnatural dynamics. In this paper, we delve deep into the noise initialization of vid
Externí odkaz:
http://arxiv.org/abs/2312.07537
Autor:
Jiang, Yuming, Wu, Tianxing, Yang, Shuai, Si, Chenyang, Lin, Dahua, Qiao, Yu, Loy, Chen Change, Liu, Ziwei
Text-driven video generation witnesses rapid progress. However, merely using text prompts is not enough to depict the desired subject appearance that accurately aligns with users' intents, especially for customized content creation. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2312.00777
Autor:
Wang, Yaohui, Chen, Xinyuan, Ma, Xin, Zhou, Shangchen, Huang, Ziqi, Wang, Yi, Yang, Ceyuan, He, Yinan, Yu, Jiashuo, Yang, Peiqing, Guo, Yuwei, Wu, Tianxing, Si, Chenyang, Jiang, Yuming, Chen, Cunjian, Loy, Chen Change, Dai, Bo, Lin, Dahua, Qiao, Yu, Liu, Ziwei
This work aims to learn a high-quality text-to-video (T2V) generative model by leveraging a pre-trained text-to-image (T2I) model as a basis. It is a highly desirable yet challenging task to simultaneously a) accomplish the synthesis of visually real
Externí odkaz:
http://arxiv.org/abs/2309.15103
Since photorealistic faces can be readily generated by facial manipulation technologies nowadays, potential malicious abuse of these technologies has drawn great concerns. Numerous deepfake detection methods are thus proposed. However, existing metho
Externí odkaz:
http://arxiv.org/abs/2309.14991
Misinformation has become a pressing issue. Fake media, in both visual and textual forms, is widespread on the web. While various deepfake detection and text fake news detection methods have been proposed, they are only designed for single-modality f
Externí odkaz:
http://arxiv.org/abs/2309.14203
Autor:
Wu, Tianxing, Cao, Xudong, Zhu, Yipeng, Wu, Feiyue, Gong, Tianling, Wang, Yuxiang, Jing, Shenqi
To easily obtain the knowledge about autism spectrum disorder and help its early screening and diagnosis, we create AsdKB, a Chinese knowledge base on autism spectrum disorder. The knowledge base is built on top of various sources, including 1) the d
Externí odkaz:
http://arxiv.org/abs/2307.16773
Existing deepfake detection methods fail to generalize well to unseen or degraded samples, which can be attributed to the over-fitting of low-level forgery patterns. Here we argue that high-level semantics are also indispensable recipes for generaliz
Externí odkaz:
http://arxiv.org/abs/2306.00863