By combining natural language understanding and the generation capabilities and breadth of knowledge of large language models with image perception, recent large vision language models (LVLMs) have shown unprecedented reasoning capabilities in the real world.
Diffusion models have opened up new avenues for the field of image generation, resulting in the proliferation of high-quality models shared on open-source platforms.
Affordance grounding refers to the task of finding the area of an object with which one can interact.
Moreover, we propose a proximity data generation (PDG) module to automatically produce more diverse data for cross-modal training.
Based on a pre-trained conditional text-to-image (T2I) diffusion model, our model aims to generate videos conditioned on a sequence of control signals, such as edge or depth maps.
Yet, current distributed RL systems tie the definition of RL algorithms to their distributed execution: they hard-code particular distribution strategies and only accelerate specific parts of the computation (e. g. policy network updates) on GPU workers.
1 code implementation • 7 Sep 2022 • Jiaxing Zhang, Ruyi Gan, Junjie Wang, Yuxiang Zhang, Lin Zhang, Ping Yang, Xinyu Gao, Ziwei Wu, Xiaoqun Dong, Junqing He, Jianheng Zhuo, Qi Yang, Yongfeng Huang, Xiayu Li, Yanghan Wu, Junyu Lu, Xinyu Zhu, Weifeng Chen, Ting Han, Kunhao Pan, Rui Wang, Hao Wang, XiaoJun Wu, Zhongshen Zeng, Chongpei Chen
We hope that this project will be the foundation of Chinese cognitive intelligence.
We propose a semi-supervised approach for contemporary object detectors following the teacher-student dual model framework.
Recent work has proposed the use of human evaluation for image synthesis models, allowing for a reliable method to evaluate the visual quality of generated images.
We experimentally demonstrate the strength of our approach over different non-hierarchical and hierarchical baselines.