no code implementations • 10 Nov 2023 • ZiHao Wang, Shaofei Cai, Anji Liu, Yonggang Jin, Jinbing Hou, Bowei Zhang, Haowei Lin, Zhaofeng He, Zilong Zheng, Yaodong Yang, Xiaojian Ma, Yitao Liang
Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents.
no code implementations • 12 Oct 2023 • Shaofei Cai, Bowei Zhang, ZiHao Wang, Xiaojian Ma, Anji Liu, Yitao Liang
We propose to follow reference videos as instructions, which offer expressive goal specifications while eliminating the need for expensive text-gameplay annotations.
1 code implementation • 3 Feb 2023 • ZiHao Wang, Shaofei Cai, Guanzhou Chen, Anji Liu, Xiaojian Ma, Yitao Liang
We investigate the challenge of task planning for multi-task embodied agents in open-world environments.
2 code implementations • CVPR 2023 • Shaofei Cai, ZiHao Wang, Xiaojian Ma, Anji Liu, Yitao Liang
We study the problem of learning goal-conditioned policies in Minecraft, a popular, widely accessible yet challenging open-ended environment for developing human-level multi-task agents.
1 code implementation • CVPR 2022 • Shaofei Cai, Liang Li, Xinzhe Han, Jiebo Luo, Zheng-Jun Zha, Qingming Huang
However, the currently used graph search space overemphasizes learning node features and neglects mining hierarchical relational information.
Ranked #2 on Link Prediction on TSP/HCP Benchmark set
no code implementations • 2 Apr 2022 • Zhenhuan Liu, Jincan Deng, Liang Li, Shaofei Cai, Qianqian Xu, Shuhui Wang, Qingming Huang
Conditional image generation is an active research topic including text2image and image translation.
Conditional Image Generation Generative Adversarial Network +1
1 code implementation • 22 Sep 2021 • Bingchuan Li, Shaofei Cai, Wei Liu, Peng Zhang, Qian He, Miao Hua, Zili Yi
To address these limitations, we design a Dynamic Style Manipulation Network (DyStyle) whose structure and parameters vary by input samples, to perform nonlinear and adaptive manipulation of latent codes for flexible and precise attribute control.
no code implementations • 3 Sep 2021 • Shaofei Cai, Liang Li, Xinzhe Han, Zheng-Jun Zha, Qingming Huang
Recently, researchers study neural architecture search (NAS) to reduce the dependence of human expertise and explore better GNN architectures, but they over-emphasize entity features and ignore latent relation information concealed in the edges.
1 code implementation • CVPR 2021 • Shaofei Cai, Liang Li, Jincan Deng, Beichen Zhang, Zheng-Jun Zha, Li Su, Qingming Huang
Inspired by the strong searching capability of neural architecture search (NAS) in CNN, this paper proposes Graph Neural Architecture Search (GNAS) with novel-designed search space.