Syllabus

Logistic, Introduction, History.

Image processing: image gradient, filter, convolution.

Classic edge/corner/line detection methods: Canny edge detection, Harris corner detection, line fitting.

Edge/line detection: Canny edge detection, line fitting(RANSAC, Hough transform).

Corner detection and feature descriptors: Harris corner detection, feature.

The outline of deep learning. Network architecture: single-layer neural network, Multi-Layer Perceptron (MLP).

Convolution layer and CNN. Conv layer vs. Fully-connected layer. Neural network training: weight initialization, loss, optimizer (gradient descent, SGD, Adam), learning rate.

Underfitting and overfitting: batch normalization, skip link, augmentation, regularization.

Classification task: KNN, SoftMax, cross entropy loss.

Classification task: receptive field, architecture(VGG, ResNet…).

Segmentation task: K-Means, upsampling, architecture (FCN, UNet).

Object detector (SSD, RCNN series, YOLO); 2D Instance Segmentation.

Camera model.

Stereo Vision, Depth and Depth Prediction, 3D Representations.

3D Deep learning & Neural 3D Reconstruction (NeRF, 3DGS)

Attention mechanism, transformer, visual transformer.

Vision language pretraining, large language model, vision language model.

GAN, VAE.

Diffusion Model.