no code implementations • 11 Feb 2024 • Tingting Wang, Guilin Qi, Tianxing Wu
To achieve this, KGroot uses event knowledge and the correlation between events to perform root cause reasoning by integrating knowledge graphs and GCNs for RCA.
no code implementations • 20 Jan 2024 • Keyu Wang, Guilin Qi, Jiaoyan Chen, Tianxing Wu
Extensional knowledge provides information about the concrete instances that belong to specific concepts in the ontology, while intensional knowledge details inherent properties, characteristics, and semantic associations among concepts.
1 code implementation • 12 Dec 2023 • Tianxing Wu, Chenyang Si, Yuming Jiang, Ziqi Huang, Ziwei Liu
Though diffusion-based video generation has witnessed rapid progress, the inference results of existing models still exhibit unsatisfactory temporal consistency and unnatural dynamics.
no code implementations • 1 Dec 2023 • Yuming Jiang, Tianxing Wu, Shuai Yang, Chenyang Si, Dahua Lin, Yu Qiao, Chen Change Loy, Ziwei Liu
In this paper, we study the task of video generation with image prompts, which provide more accurate and direct content control beyond the text prompts.
1 code implementation • 29 Nov 2023 • Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, Yaohui Wang, Xinyuan Chen, LiMin Wang, Dahua Lin, Yu Qiao, Ziwei Liu
We will open-source VBench, including all prompts, evaluation methods, generated videos, and human preference annotations, and also include more video generation models in VBench to drive forward the field of video generation.
2 code implementations • 26 Sep 2023 • Yaohui Wang, Xinyuan Chen, Xin Ma, Shangchen Zhou, Ziqi Huang, Yi Wang, Ceyuan Yang, Yinan He, Jiashuo Yu, Peiqing Yang, Yuwei Guo, Tianxing Wu, Chenyang Si, Yuming Jiang, Cunjian Chen, Chen Change Loy, Bo Dai, Dahua Lin, Yu Qiao, Ziwei Liu
To this end, we propose LaVie, an integrated video generation framework that operates on cascaded video latent diffusion models, comprising a base T2V model, a temporal interpolation model, and a video super-resolution model.
Ranked #4 on Text-to-Video Generation on EvalCrafter Text-to-Video (ECTV) Dataset (using extra training data)
1 code implementation • 26 Sep 2023 • Rui Shao, Tianxing Wu, Ziwei Liu
However, existing methods only focus on detecting one-step facial manipulation.
1 code implementation • 25 Sep 2023 • Rui Shao, Tianxing Wu, Jianlong Wu, Liqiang Nie, Ziwei Liu
HAMMER performs 1) manipulation-aware contrastive learning between two uni-modal encoders as shallow manipulation reasoning, and 2) modality-aware cross-attention by multi-modal aggregator as deep manipulation reasoning.
no code implementations • 31 Jul 2023 • Tianxing Wu, Xudong Cao, Yipeng Zhu, Feiyue Wu, Tianling Gong, Yuxiang Wang, Shenqi Jing
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.
1 code implementation • 1 Jun 2023 • Rui Shao, Tianxing Wu, Liqiang Nie, Ziwei Liu
Unlike existing deepfake detection methods merely focusing on low-level forgery patterns, the forgery detection process of our model can be regularized by generalizable high-level semantics from a pre-trained ViT and adapted by global and local low-level forgeries of deepfake data.
1 code implementation • CVPR 2023 • Rui Shao, Tianxing Wu, Ziwei Liu
In this paper, we highlight a new research problem for multi-modal fake media, namely Detecting and Grounding Multi-Modal Media Manipulation (DGM^4).
2 code implementations • 23 Mar 2023 • Ziqi Huang, Tianxing Wu, Yuming Jiang, Kelvin C. K. Chan, Ziwei Liu
Specifically, we propose a novel relation-steering contrastive learning scheme to impose two critical properties of the relation prompt: 1) The relation prompt should capture the interaction between objects, enforced by the preposition prior.
1 code implementation • 5 Jul 2022 • Rui Shao, Tianxing Wu, Ziwei Liu
Moreover, we build a comprehensive benchmark and set up rigorous evaluation protocols and metrics for this new research problem.
2 code implementations • 1 Nov 2021 • Yongrui Chen, Huiying Li, Guilin Qi, Tianxing Wu, Tenggou Wang
The high-level decoding generates an AQG as a constraint to prune the search space and reduce the locally ambiguous query graph.
no code implementations • 15 Oct 2019 • Tianxing Wu, Arijit Khan, Melvin Yong, Guilin Qi, Meng Wang
Knowledge graph (KG) embedding encodes the entities and relations from a KG into low-dimensional vector spaces to support various applications such as KG completion, question answering, and recommender systems.
2 code implementations • 15 Oct 2019 • Yu-Xiang Wang, Arijit Khan, Tianxing Wu, Jiahui Jin, Haijiang Yan
We face two challenges on graph query over a knowledge graph: (1) the structural gap between $G_Q$ and the predefined schema in $G$ causes mismatch with query graph, (2) users cannot view the answers until the graph query terminates, leading to a longer system response time (SRT).
Databases