ObjectDetectionTrackingRecognition (with deep learning)
under construction ...
Visual Detection, Recognition and Tracking with Deep Learning
•Deep learning
•Sparse coding
•Deep models
•CNN/NIN/RNN;
•DBN/DBM;
•Stacked DAE;
•Optimization/Learning methods
•SGD & BP;
•AdaGrad/AdaDelta
•Dropout/Maxout
•Data Augmentation
•MCMC/Mean Field
•Contrastive Divergence
•Wake-Sleep
•Greedy layer-wise pre-training
•Model Compression
•Dark knowledge;
•Distilling the knowledge.
•Visual recognition
•Sparse coding
•Hierarchical feature learning
•LeNet/Alexnet/Mattnet(ZFNet);
•VGG Net;
•GoogleNet
•PReLU;
•Batch normalization;
•Rethink the Inception;
•Deep Residual Learning;
•Generic object detection
•Deep multi-box;
•OverFeat;
•R-CNN/Fast R-CNN/Faster R-CNN;
•SPP Net;
•DeepID-Net;
•YOLO;
•DeepBox;
•Region-based FCN.
•Pedestrian detection
•Pose estimation
•Face detection, landmark detection and recognition
•Text detection and recognition
•Scene parsing/Semantic segmentation
•Multiscale Feature Learning;
•Simultaneous Detection and Segmentation;
•FCN/DeepLab/Parsenet/Segnet.
•Visual tracking
•Appendix A: SoC implementation of CNN