no code implementations • ECCV 2020 • Lida Li, Kun Wang, Shuai Li, Xiangchu Feng, Lei Zhang
The 2D convolutional (Conv2d) layer is the fundamental element to a deep convolutional neural network (CNN).
2 code implementations • Proceedings of Machine Learning Research 2022 • Lingxiao Yang, Ru-Yuan Zhang, Lida Li, Xiaohua Xie
Another advantage of the module is that most of the operators are selected based on the solution to the defined energy function, avoiding too many efforts for structure tuning.
no code implementations • 18 Mar 2022 • Lida Li, Shuai Li, Kun Wang, Xiangchu Feng, Lei Zhang
2D convolution (Conv2d), which is responsible for extracting features from the input image, is one of the key modules of a convolutional neural network (CNN).
1 code implementation • CVPR 2021 • Minghan Li, Shuai Li, Lida Li, Lei Zhang
To further explore temporal correlation among video frames, we aggregate a temporal fusion module to infer instance masks from each frame to its adjacent frames, which helps our framework to handle challenging videos such as motion blur, partial occlusion and unusual object-to-camera poses.
Ranked #24 on Video Instance Segmentation on YouTube-VIS 2021
1 code implementation • 30 Sep 2020 • Hui Zeng, Jianrui Cai, Lida Li, Zisheng Cao, Lei Zhang
The small CNN works on the down-sampled version of the input image to predict content-dependent weights to fuse the multiple basis 3D LUTs into an image-adaptive one, which is employed to transform the color and tone of source images efficiently.
Ranked #5 on Image Enhancement on MIT-Adobe 5k (SSIM on proRGB metric)
1 code implementation • 18 Sep 2019 • Hui Zeng, Lida Li, Zisheng Cao, Lei Zhang
The employed evaluation metrics such as intersection-over-union cannot reliably reflect the real performance of a cropping model, either.
1 code implementation • CVPR 2019 • Hui Zeng, Lida Li, Zisheng Cao, Lei Zhang
Consequently, a grid anchor based cropping benchmark is constructed, where all crops of each image are annotated and more reliable evaluation metrics are defined.