Code accompanying the paper Optimizing the F-measure for Threshold-free Salient Object Detection.
The released code of AAAI2019 paper "Image Saliency Prediction in Transformed Domain: A Deep Complex Neural Network Method"
MSNet Code for paper in CVPR2019, 'A Mutual Learning Method for Salient Object Detection with intertwined Multi-Supervision'
Code "SdBAN: Salient Object Detection Using Bilateral Attention Network with Dice Coefficient Loss"
For paper "PointCloud Saliency Maps" (ICCV 2019 oral presentation, Acceptance Rate: 4.3%)
PyTorch implementation of Multi-Label Image Recognition with Graph Convolutional Networks, CVPR 2019.
There is a paper in CVPR 2019 about the saliency detection. The source code of this paper is using the keras framework. We transform them into pytorch framework.
Traffic sign recognition is an advanced driver assistance system which can recognize road signs and display the corresponding information in the vehicle. It consists of two parts, Sign Detection and Sign Classification. In detection we will detect the coordinates and size of sign board in the image and in classification we try to find that what this signal represent or what class this signal belongs. In our case there are eight classes of sign boards that need to classify they are shown below:
Experimenting with sort different classical tracking algorithms for real time multiple object tracking (MOT)
code and model of Pyramid Feature Selective Network for Saliency detection
