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