InternChat: Solving Vision-Centric Tasks by Interacting with Chatbots
  Beyond Language
Authors: name: Zhaoyang Liu, name: Yinan He, name: Wenhai Wang, name: Weiyun Wang, name: Yi Wang, name: Shoufa Chen, name: Qinglong Zhang, name: Yang Yang, name: Qingyun Li, name: Jiashuo Yu, name: Kunchang Li, name: Zhe Chen, name: Xue Yang, name: Xizhou Zhu, name: Yali Wang, name: Limin Wang, name: Ping Luo, name: Jifeng Dai, name: Yu Qiao

We present an interactive visual framework named InternChat, or iChat for
short. The framework integrates chatbots that have planning and reasoning
capabilities, such as ChatGPT, with non-verbal instructions like pointing
movements that enable users to directly manipulate images or videos on the
screen. Pointing (including gestures, cursors, etc.) movements can provide more
flexibility and precision in performing vision-centric tasks that require
fine-grained control, editing, and generation of visual content. The name
InternChat stands for interaction, nonverbal, and chatbots. Different from
existing interactive systems that rely on pure language, by incorporating
pointing instructions, the proposed iChat significantly improves the efficiency
of communication between users and chatbots, as well as the accuracy of
chatbots in vision-centric tasks, especially in complicated visual scenarios
where the number of objects is greater than 2. Additionally, in iChat, an
auxiliary control mechanism is used to improve the control capability of LLM,
and a large vision-language model termed Husky is fine-tuned for high-quality
multi-modal dialogue (impressing ChatGPT-3.5-turbo with 93.89% GPT-4 Quality).
We hope this work can spark new ideas and directions for future interactive
visual systems. Welcome to watch the code at
https://github.com/OpenGVLab/InternChat.