Deep-Learning-For-Computer-Vision

Official Repo for Deep Learning for Compyter Vision Course offered by NPTEL

Fall 2022 Link : https://onlinecourses.nptel.ac.in/noc22_cs76/preview

Lectures: https://www.youtube.com/watch?v=rfAvjCf1_ZI&list=PLyqSpQzTE6M_PI-rIz4O1jEgffhJU9GgG

Course Cirriculum

Week 1:Introduction and Overview:

Course Overview and Motivation; Introduction to Image Formation, Capture and Representation; Linear Filtering, Correlation, Convolution

Week 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 Flow

Week 4:Deep Learning Review:

Review of Deep Learning, Multi-layer Perceptrons, Backpropagation

Week 5:Convolutional Neural Networks (CNNs):

Introduction to CNNs; Evolution of CNN Architectures: AlexNet, ZFNet, VGG, InceptionNets, ResNets, DenseNets

Week 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-RCNN

Week 8:Recurrent Neural Networks (RNNs):

Review of RNNs; CNN + RNN Models for Video Understanding: Spatio-temporal Models, Action/Activity Recognition

Week 9:Attention Models:

Introduction to Attention Models in Vision; Vision and Language: Image Captioning, Visual QA, Visual Dialog; Spatial Transformers; Transformer Networks

Week 10:Deep Generative Models:

Review of (Popular) Deep Generative Models: GANs, VAEs; Other Generative Models: PixelRNNs, NADE, Normalizing Flows, etc

Week 11:Variants and Applications of Generative Models in Vision:

Applications: Image Editing, Inpainting, Superresolution, 3D Object Generation, Security; Variants: CycleGANs, Progressive GANs, StackGANs, Pix2Pix, etc

Week 12:Recent Trends:

Zero-shot, One-shot, Few-shot Learning; Self-supervised Learning; Reinforcement Learning in Vision; Other Recent Topics and Applications