Deep Learning for Computer Vision

Por: Swayam . en: , ,

Week 1:Introduction and Overview:○ Course Overview and Motivation; Introduction to Image Formation, Capture and Representation; Linear Filtering, Correlation, ConvolutionWeek 2:Visual Features and Representations:○ Edge, Blobs, Corner Detection; Scale Space and Scale Selection; SIFT, SURF; HoG, LBP, etc.Week 3:Visual Matching:○ Bag-of-words, VLAD; RANSAC, Hough transform; Pyramid Matching; Optical FlowWeek 4:Deep Learning Review:○ Review of Deep Learning, Multi-layer Perceptrons, BackpropagationWeek 5:Convolutional Neural Networks (CNNs):○ Introduction to CNNs; Evolution of CNN Architectures: AlexNet, ZFNet, VGG, InceptionNets, ResNets, DenseNetsWeek 6:Visualization and Understanding CNNs:○ Visualization of Kernels; Backprop-to-image/Deconvolution Methods; Deep Dream, Hallucination, Neural Style Transfer; CAM,Grad-CAM, Grad-CAM++; Recent Methods (IG, Segment-IG, SmoothGrad)Week 7:CNNs for Recognition, Verification, Detection, Segmentation:○ CNNs for Recognition and Verification (Siamese Networks, Triplet Loss, Contrastive Loss, Ranking Loss); CNNs for Detection: Background of Object Detection, R-CNN, Fast R-CNN, Faster R-CNN, YOLO, SSD, RetinaNet; CNNs for Segmentation: FCN, SegNet, U-Net, Mask-RCNNWeek 8:Recurrent Neural Networks (RNNs):○ Review of RNNs; CNN + RNN Models for Video Understanding: Spatio-temporal Models, Action/Activity RecognitionWeek 9:Attention Models:○ Introduction to Attention Models in Vision; Vision and Language: Image Captioning, Visual QA, Visual Dialog; Spatial Transformers; Transformer NetworksWeek 10:Deep Generative Models:○ Review of (Popular) Deep Generative Models: GANs, VAEs; Other Generative Models: PixelRNNs, NADE, Normalizing Flows, etcWeek 11:Variants and Applications of Generative Models in Vision:○ Applications: Image Editing, Inpainting, Superresolution, 3D Object Generation, Security; Variants: CycleGANs, Progressive GANs, StackGANs, Pix2Pix, etcWeek 12:Recent Trends:○ Zero-shot, One-shot, Few-shot Learning; Self-supervised Learning; Reinforcement Learning in Vision; Other Recent Topics and Applications

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