no code implementations • 18 Dec 2023 • Shuailei Ma, Chen-Wei Xie, Ying WEI, Siyang Sun, Jiaqi Fan, Xiaoyi Bao, Yuxin Guo, Yun Zheng
In this paper, we conduct a direct analysis of the multi-modal prompts by asking the following questions: $(i)$ How do the learned multi-modal prompts improve the recognition performance?
1 code implementation • 14 Dec 2023 • Shuailei Ma, Yuefeng Wang, Ying WEI, Jiaqi Fan, Enming Zhang, Xinyu Sun, Peihao Chen
Ablation experiments demonstrate that both of them are effective in mitigating the impact of open-world knowledge distillation on the learning of known objects.
1 code implementation • 21 Mar 2023 • Shuailei Ma, Yuefeng Wang, Ying WEI, Peihao Chen, Zhixiang Ye, Jiaqi Fan, Enming Zhang, Thomas H. Li
We propose leveraging the VL as the ``Brain'' of the open-world detector by simply generating unknown labels.
no code implementations • 8 Jan 2023 • Shuai Wang, ChiYung Yam, Shuguang Chen, Lihong Hu, Liping Li, Faan-Fung Hung, Jiaqi Fan, Chi-Ming Che, Guanhua Chen
Here, we develop a general protocol for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield for phosphorescent Pt(II) emitters based on the combination of first-principles quantum mechanical method, machine learning (ML) and experimental calibration.
no code implementations • CVPR 2023 • Shuailei Ma, Yuefeng Wang, Jiaqi Fan, Ying WEI, Thomas H. Li, Hongli Liu, Fanbing Lv
Open-world object detection (OWOD), as a more general and challenging goal, requires the model trained from data on known objects to detect both known and unknown objects and incrementally learn to identify these unknown objects.
1 code implementation • 9 Jul 2021 • Wen Shen, Zhihua Wei, Shikun Huang, BinBin Zhang, Jiaqi Fan, Ping Zhao, Quanshi Zhang
The reasonable definition of semantic interpretability presents the core challenge in explainable AI.
no code implementations • 10 Jul 2020 • Chen Liu, Jiaqi Fan, Guosheng Yin
Image dehazing without paired haze-free images is of immense importance, as acquiring paired images often entails significant cost.