no code implementations • EMNLP 2021 • Xinmeng Li, Qian Li, Wansen Wu, Quanjun Yin
Recently, the focus of dialogue state tracking has expanded from single domain to multiple domains.
no code implementations • 29 Nov 2023 • Ting Liu, Yue Hu, Wansen Wu, Youkai Wang, Kai Xu, Quanjun Yin
Then we introduce soft visual prompts in the input space of the visual encoder in a pretrained model.
no code implementations • 7 Sep 2023 • Ting Liu, Yue Hu, Wansen Wu, Youkai Wang, Kai Xu, Quanjun Yin
In the indoor-aware stage, we apply an efficient tuning paradigm to learn deep visual prompts from an indoor dataset, in order to augment pretrained models with inductive biases towards indoor environments.
no code implementations • 15 Mar 2023 • Guanghao Li, Wansen Wu, Yan Sun, Li Shen, Baoyuan Wu, DaCheng Tao
Then, the local model is trained on the input composed of raw data and a visual prompt to learn the distribution information contained in the prompt.
no code implementations • 26 Aug 2021 • Wansen Wu, Tao Chang, Xinmeng Li
This paper provides a comprehensive survey and an insightful taxonomy of these tasks based on the different characteristics of language instructions in these tasks.
no code implementations • 3 Aug 2021 • Xinmeng Li, Wansen Wu, Long Qin, Quanjun Yin
Evaluating the quality of a dialogue system is an understudied problem.