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Official Repo for Deep Learning for Compyter Vision Course offered by NPTEL
DL4CV-NPTEL/Deep-Learning-For-Computer-Vision
Folders and files, repository files navigation, deep-learning-for-computer-vision, fall 2022 link : https://onlinecourses.nptel.ac.in/noc22_cs76/preview, lectures: https://www.youtube.com/watchv=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
Contributors 3
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