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Introduction and Overview:
Course Overview and Motivation; Introduction to Image Formation, Capture and
Representation; Linear Filtering, Correlation, Convolution
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Visual Features and Representations:
Edge, Blobs, Corner Detection; Scale Space and Scale Selection; SIFT, SURF; HoG,LBP, etc.
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Visual Matching:
Bag-of-words, VLAD; RANSAC, Hough transform; Pyramid Matching; Optical Flow
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Deep Learning Review:
Review of Deep Learning, Multi-layer Perceptrons, Backpropagation
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Convolutional Neural Networks (CNNs):
Introduction to CNNs; Evolution of CNN Architectures: AlexNet, ZFNet, VGG,
InceptionNets, ResNets, DenseNets
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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)
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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
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Recurrent Neural Networks (RNNs):
Review of RNNs; CNN + RNN Models for Video Understanding: Spatio-temporal
Models, Action/Activity Recognition
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Attention Models:
Introduction to Attention Models in Vision; Vision and Language: Image Captioning,
Visual QA, Visual Dialog; Spatial Transformers; Transformer Networks
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Deep Generative Models:
Review of (Popular) Deep Generative Models: GANs, VAEs; Other Generative Models:
PixelRNNs, NADE, Normalizing Flows, etc
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Variants and Applications of Generative Models in Vision:
Applications: Image Editing, Inpainting, Superresolution, 3D Object Generation, Security;
Variants: CycleGANs, Progressive GANs, StackGANs, Pix2Pix, etc
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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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