1 code implementation • 18 Jan 2024 • Xiaohu Jiang, Yixiao Ge, Yuying Ge, Dachuan Shi, Chun Yuan, Ying Shan
Image-text training like CLIP has dominated the pretraining of vision foundation models in recent years.
1 code implementation • 27 May 2023 • Dachuan Shi, Chaofan Tao, Anyi Rao, Zhendong Yang, Chun Yuan, Jiaqi Wang
Although extensively studied for unimodal models, the acceleration for multimodal models, especially the vision-language Transformers, is relatively under-explored.
1 code implementation • 31 Jan 2023 • Dachuan Shi, Chaofan Tao, Ying Jin, Zhendong Yang, Chun Yuan, Jiaqi Wang
Real-world data contains a vast amount of multimodal information, among which vision and language are the two most representative modalities.
1 code implementation • International Conference on Acoustics, Speech and Signal Processing 2022 • Dachuan Shi, Ruiyang Liu, Linmi Tao, Chun Yuan
This manuscript goes deep into the research of the Dropout algorithm, which is commonly used in neural networks to alleviate the overfitting problem.
3 code implementations • 3 May 2022 • Zhendong Yang, Zhe Li, Mingqi Shao, Dachuan Shi, Zehuan Yuan, Chun Yuan
The current distillation algorithm usually improves students' performance by imitating the output of the teacher.
1 code implementation • International Conference on Image Processing 2021 • Dachuan Shi, Ruiyang Liu, Linmi Tao, Zuoxiang He, Li Huo
We aim on enhancing medical image segmentation by using spatial continuity information in a proposed Multi-Encoder Parse-Decoder Network (MEPDNet) based on the fact that most of the medical images are sampled continuously.
1 code implementation • 17 Jan 2021 • Dachuan Shi, Eldar Sabanovic, Luca Rizzetto, Viktor Skrickij, Roberto Oliverio, Nadia Kaviani, Yunguang Ye, Gintautas Bureika, Stefano Ricci, Markus Hecht
To tackle this issue, we propose virtual point tracking for real-time target-less dynamic displacement measurement, incorporating deep learning techniques and domain knowledge.
no code implementations • 24 Jul 2020 • Dachuan Shi, Yunguang Ye, Marco Gillwald, Markus Hecht
In comparison to the state-of-the-art lightweight CNNs, LightWFNet is validated for WF detection by using carbody accelerations with much lower computational costs.