no code implementations • 9 Nov 2022 • Zhuoqun Liu, Meiguang Jin, Ying Chen, Huaida Liu, Canqian Yang, Hongkai Xiong
In this paper, we identify the real bottlenecks that affect the CNN-based models' run-time performance on mobile devices: memory access cost and NPU-incompatible operations, and build the model based on these.
1 code implementation • 18 Jul 2022 • Canqian Yang, Meiguang Jin, Yi Xu, Rui Zhang, Ying Chen, Huaida Liu
Image-adaptive lookup tables (LUTs) have achieved great success in real-time image enhancement tasks due to their high efficiency for modeling color transforms.
Ranked #5 on Image Enhancement on MIT-Adobe 5k (PSNR on proRGB metric)
1 code implementation • CVPR 2022 • Canqian Yang, Meiguang Jin, Xu Jia, Yi Xu, Ying Chen
They adopt a sub-optimal uniform sampling point allocation, limiting the expressiveness of the learned LUTs since the (tri-)linear interpolation between uniform sampling points in the LUT transform might fail to model local non-linearities of the color transform.
Ranked #2 on Photo Retouching on MIT-Adobe 5k
no code implementations • 20 Apr 2022 • Tiancheng Lin, Hongteng Xu, Canqian Yang, Yi Xu
When applying multi-instance learning (MIL) to make predictions for bags of instances, the prediction accuracy of an instance often depends on not only the instance itself but also its context in the corresponding bag.
no code implementations • 18 Apr 2022 • Yuanfan Guo, Canqian Yang, Tiancheng Lin, Chunxiao Li, Rui Zhang, Yi Xu
Since an ultrasound image only describes a partial 2D projection of a 3D lesion, such paradigm ignores the semantic relationship between different views of a lesion, which is inconsistent with the traditional diagnosis where sonographers analyze a lesion from at least two views.
no code implementations • 9 Apr 2020 • Tiancheng Lin, Yuanfan Guo, Canqian Yang, Jiancheng Yang, Yi Xu
Early diagnosis of signet ring cell carcinoma dramatically improves the survival rate of patients.
2 code implementations • 24 Nov 2019 • Jiancheng Yang, Xiaoyang Huang, Yi He, Jingwei Xu, Canqian Yang, Guozheng Xu, Bingbing Ni
Theoretically, ANY 2D CNN (ResNet, DenseNet, or DeepLab) is able to be converted into a 3D ACS CNN, with pretrained weight of a same parameter size.