Published 1/2023 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 3.21 GB | Duration: 5h 31m Transformers in Computer Vision - English version
What you'll learn What are transformer networks? State of the Art architectures for CV Apps like Image Classification, Semantic Segmentation, Object Detection and Video Processing Practical application of SoTA architectures like ViT, DETR, SWIN in Huggingface vision transformers Attention mechanisms as a general Deep Learning idea Inductive Bias and the landscape of DL models in terms of modeling assumptions Transformers application in NLP and Machine Translation Transformers in Computer Vision Different types of attention in Computer Vision Requirements Practical Machine Learning course Practical Computer Vision course (ConvNets) Introduction to NLP course Description Transformer Networks are the new trend in Deep Learning nowadays. Transformer models have taken the world of NLP by storm since 2017. Since then, they become the mainstream model in almost ALL NLP tasks. Transformers in CV are still lagging, however they started to take over since 2020. We will start by introducing attention and the transformer networks. Since transformers were first introduced in NLP, they are easier to be described with some NLP example first. From there, we will understand the pros and cons of this architecture. Also, we will discuss the importance of unsupervised or semi supervised pre-training for the transformer architectures, discussing Large Scale Language Models (LLM) in brief, like BERT and GPT.This will pave the way to introduce transformers in CV. Here we will try to extend the attention idea into the 2D spatial domain of the image. We will discuss how convolution can be generalized using self attention, within the encoder-decoder meta architecture. We will see how this generic architecture is almost the same in image as in text and NLP, which makes transformers a generic function approximator. We will discuss the channel and spatial attention, local vs. global attention among other topics.In the next three modules, we will discuss the specific networks that solve the big problems in CV: classification, object detection and segmentation. We will discuss Vision Transformer (ViT) from Google, Shifter Window Transformer (SWIN) from Microsoft, Detection Transformer (DETR) from Facebook research, Segmentation Transformer (SETR) and many others. Then we will discuss the application of Transformers in video processing, through Spatio-Temporal Transformers with application to Moving Object Detection, along with Multi-Task Learning setup.Finally, we will show how those pre-trained arcthiectures can be easily applied in practice using the famous Huggingface library using the Pipeline interface. Overview Section 1: Introduction Lecture 1 Introduction Section 2: Overview of Transformer Networks Lecture 2 The Rise of Transformers Lecture 3 Inductive Bias in Deep Neural Network Models Lecture 4 Attention is a General DL idea Lecture 5 Attention in NLP Lecture 6 Attention is ALL you need Lecture 7 Self Attention Mechanisms Lecture 8 Self Attention Matrix Equations Lecture 9 Multihead Attention Lecture 10 Encoder-Decoder Attention Lecture 11 Transformers Pros and Cons Lecture 12 Unsupervised Pre-training Section 3: Transformers in Computer Vision Lecture 13 Module roadmap Lecture 14 Encoder-Decoder Design Pattern Lecture 15 Convolutional Encoders Lecture 16 Self Attention vs. Convolution Lecture 17 Spatial vs. Channel vs. Temporal Attention Lecture 18 Generalization of self attention equations Lecture 19 Local vs. Global Attention Lecture 20 Pros and Cons of Attention in CV Section 4: Transformers in Image Classification Lecture 21 Transformers in image classification Lecture 22 Vistion Transformers (ViT and DeiT) Lecture 23 Shifted Window Transformers (SWIN) Section 5: Transformers in Object Detection Lecture 24 Transformers in Object detection Lecture 25 Obejct Detection methods review Lecture 26 Object Detection with ConvNet - YOLO Lecture 27 DEtection TRansformers (DETR) Lecture 28 DETR vs. YOLOv5 use case Section 6: Transformers in Semantic Segmentation Lecture 29 Module roadmap Lecture 30 Image Segmentation using ConvNets Lecture 31 Image Segmentation using Transformers Section 7: Spatio-Temporal Transformers Lecture 32 Spatio-Temporal Transformers - Moving Object Detection and Multi-trask Learning Section 8: Huggingface Vision Transformers Lecture 33 Module roadmap Lecture 34 Huggingface Pipeline overview Lecture 35 Huggingface vision transformers Lecture 36 Huggingface Demo using Gradio Section 9: Conclusion Lecture 37 Course conclusion Section 10: Material Lecture 38 Slides Intermediate to Advanced CV Engineers,Intermediate to Advanced CV Researchers
