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1
Course Introduction
2
History
3
Image Formation
4
Image Representation
5
Linear Filtering
6
Image in Frequency Domain
7
Image Sampling
8
Edge Detection
9
From Edges to Blobs and Corners
10
Scale Space, Image Pyramids and Filter Banks
11
Feature Detectors : SIFT and Variants
12
Image Segmentation
13
Other Feature Spaces
14
Human Visual System
15
Feature Matching
16
Hough Transform
17
From Points to Images:Bag-of-Words and VLAD Representations
18
Image Descriptor Matching
19
Pyramid Matching
20
From Traditional Vision to Deep Learning
21
Neural Networks: A Review - Part 1
22
Neural Networks: A Review - Part 2
23
Feedforward Neural Networks and Backpropagation - Part 1
24
Feedforward Neural Networks and Backpropagation - Part 2
25
Gradient Descent and Variants - Part 1
26
Gradient Descent and Variants - Part 2
27
Regularization in Neural Networks - Part 1
28
Regularization in Neural Networks - Part 2
29
Improving Training of Neural Networks - Part 1
30
Improving Training of Neural Networks - Part 2
31
Convolutional Neural Networks: An Introduction - Part 01
32
Convolutional Neural Networks: An Introduction - Part 02
33
Backpropagation in CNNs
34
Evolution of CNN Architectures for Image Classification-Part 01
35
Evolution of CNN Architectures for Image Classification-Part 02
36
Recent CNN Architectures
37
Finetuning in CNNs
38
Explaining CNNs: Visualization Methods
39
Explaining CNNs: Early Methods
40
Explaining CNNs: Class Attribution Map Methods
41
Explaining CNNs: Recent Methods - Part 01
42
Explaining CNNs: Recent Methods -Part 02
43
Going Beyond Explaining CNNs
44
CNNs for Object Detection I PART 01
45
CNNs for Object Detection I PART 02
46
CNNs for Object Detection II
47
CNNs for Segmentation
48
CNNs for Human Understanding Faces- Part 01
49
CNNs for Human Understanding Faces PART 02
50
CNNs for Human Understanding Human Pose and Crowd
51
CNNs for Other Image Tasks
52
Recurrent Neural Networks Introduction
53
Backpropagation in RNNs
54
LSTMs and GRUs
55
Video Understanding using CNNs and RNNs
56
Attention in Vision Models: An Introduction
57
Vision and Language: Image Captioning
58
Beyond Captioning: Visual QA, Visual Dialog
59
Other Attention Models
60
Self-Attention and Transformers
61
Deep Generative Models: An Introduction
62
Generative Adversarial Networks-Part 01
63
Generative Adversarial Networks-Part 02
64
Variational Autoencoders
65
Combining VAEs and GANs
66
Beyond VAEs and GANs: Other Deep Generative Models-01
67
Beyond VAEs and GANs: Other Deep Generative Models-02
68
GAN Improvements
69
Deep Generative Models across Multiple Domains
70
VAEs and DIsentanglement
71
Deep Generative Models: Image Applications
72
Deep Generative Models: Video Applications
73
Few-shot and Zero-shot Learning - Part 01
74
Few-shot and Zero-shot Learning - Part 02
75
Self-Supervised Learning
76
Adversarial Robustness
77
Pruning and Model Compression
78
Neural Architecture Search
79
Course Conclusion
Description:
COURSE OUTLINE: The automatic analysis and understanding of images and videos, a field called Computer Vision, occupies significant importance in applications including security, healthcare, entertainment, mobility, etc. The recent success of deep learning methods has revolutionized the field of computer vision, making new developments increasingly closer to deployment that benefits end users.

Deep Learning for Computer Vision

NPTEL
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#Computer Science #Artificial Intelligence #Computer Vision #Deep Learning #Neural Networks #Machine Learning #Self-supervised Learning #Image Processing #Image Analysis
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Course Introduction