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Found 7 papers, 5 papers with code
LST-Net: Learning a Convolutional Neural Network with a Learnable Sparse Transform
The 2D convolutional (Conv2d) layer is the fundamental element to a deep convolutional neural network (CNN).
SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks
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.
Towards Robust 2D Convolution for Reliable Visual Recognition
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).
Spatial Feature Calibration and Temporal Fusion for Effective One-stage Video Instance Segmentation
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
Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time
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)
Grid Anchor based Image Cropping: A New Benchmark and An Efficient Model
The employed evaluation metrics such as intersection-over-union cannot reliably reflect the real performance of a cropping model, either.
Reliable and Efficient Image Cropping: A Grid Anchor based Approach
Consequently, a grid anchor based cropping benchmark is constructed, where all crops of each image are annotated and more reliable evaluation metrics are defined.
