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Institute of Computer Vision
Public Account ID｜ComputerVisionGzq
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Computer Vision Research Institute column
Author: Edison_G
Today I recommend "Computer Vision Research Institute" to everyone
The Institute of Computer Vision mainly involves the fields of machine learning, deep learning, etc. It is composed of a team of postgraduates from various schools, mainly dedicated to target detection | target recognition | target tracking | image segmentation | model quantification | model deployment | and other research direction.
Page link:
http://mp.weixin.qq.com/mp/homepage?__biz=MzU0NTAyNTQ1OQ==&hid=4&sn=8ede0e9a5a32f8aa33d66590e06f491f&scene=18#wechat_redirect
The purpose of the official account " Computer Vision Research Institute " is: learn together and make progress together!
Our principle is: only do original work, and give everyone the best analysis!
The official account has written 1000+ original articles, and those who are interested can join us to learn together!
The content of the official account is very rich, mainly related to the foundation of deep learning of machine learning, knowledge in the field of computer vision and practical operation of related algorithms. The specific content of the release is as follows:
Machine Learning Basics
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[ML] Headache regularization term
[ML] Image Classification Notes (Part 1)
[ML] Image Classification Notes (Part 2)
[ML] Linear Classification Notes (Part 1)
[ML] Linear Classification Notes (Medium)
[ML] Linear Classification Notes (Part 2)
[ML] Optimization Notes (Part 1)
[ML] Optimization Notes (Part 2)
【ML】Easy mistakes in machine learning
[ML] 5 mistakes that are easy to make in the entry stage
Deep Learning Basics
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【DL】NVIDIA DIGITS
[DL] Caffe source code --------- main framework introduction
[DL] Caffe source code---Blob basic use
[DL] Simple understanding of deep learning hyperparameters (modified version)
[DL] Deep Learning --- A Specific Case of Backpropagation
[DL] Deep Learning - Receptive Field
[DL] The reason why the ReLU deep network can approximate any function
[DL] Recently popular activation function
[DL] How to become a successful "alchemist" - DL training skills
[DL] Misunderstandings in the introduction of deep learning
[DL] Various types of gradient optimization
[DL] Be careful about the "pit" of deep learning (detailed version of entry misunderstandings)
[DL] Forward Propagation of Convolutional Neural Networks
[DL] Backpropagation of Convolutional Neural Networks
[DL] Machine learning ------ a headache regularization term
Deep Learning Basic Literature
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[DL Basics] Talk about the essence of CNN in detail
[DL Basics] The transformation of the deep network "from ancient times to the present"
[DL Basics] Prospects of Deep Learning
[DL Basics] Recent Summary and Analysis of Deep Learning
[DL Basics] The future development of DL framework, TensorFlow/MXNet/Torch, which one to choose?
[DL Basics] What is the meaning of "depth" in deep learning?
[DL Basics] Yesterday, Today and Tomorrow of Deep Learning
[DL Basics] How to explain to non-professionals what deep learning is?
[DL Basics] Jia Yangqing and Caffe
[DL Basics] Deep Learning - Artificial Neural Network Research Upsurge
[DL Foundation] Brain Science Principles in Neural Network Mechanism
[DL Basics] 13 cheat sheets necessary for getting started with deep learning (download attached)
[DL Basics] Five Cases, Three Experiences——Taking You the Road to Practical Application of Advanced Deep Learning
【DL Basics】Resources | Introduction to Deep Learning and Learning Books
[DL Basics] Pure dry goods | A review of deep learning research
[DL Basics] Super dry goods | From neurons to CNN, RNN, GAN... Neural network is definitely enough to read this article
[DL Basics] A comprehensive analysis of CNN (takes you to get started easily)
[DL Basics] What can be learned from Bounding Boxes?
[DL Basics] Why can the Deep Learning (deep learning) neural network be recognized?
[DL Foundation] The latest review of deep learning optimization algorithms in 2017
Object Detection & Recognition
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[Target Detection & Recognition] Multi-scale Feature Fusion: Learning Better Semantic Information for Detection (Paper Download Attached) Target Detection in Unsupervised Learning
[Target Detection & Recognition] Yolo's lightweight network, ultra-light algorithm can achieve industrial-grade detection results on various hardware (with source code)
[Target detection & recognition] The Yolo framework has been greatly improved | A new framework for target detection with extremely low consumption (with paper download)
[Target Detection & Recognition] New technology: Efficient self-supervised visual pre-training, no need to worry about partial occlusions!
[Target Detection & Recognition] What drives the effective detection of candidate targets?
[Target Detection & Recognition] This allows for more accurate target detection - super network
[Target Detection & Recognition] CVPR: IoU Optimization - Improve target detection accuracy in Anchor-Free (with source code)
[Target Detection & Recognition] Wow~ Such a deep and lightweight Network, real-time target detection
[Target Detection & Recognition] Dry goods | Progress in target recognition algorithms
[Target Detection & Recognition] Tencent Lab: Revitalize the CNN backbone network with Transformer (with the source code download of the paper)
[Target detection & recognition] YOLOS: Rethink Transformer through target detection (with source code)
[Target Detection & Recognition] Actual Combat - Target Detection and Recognition
[Target Detection & Recognition] Topic 1 on Target Detection and Recognition | Research on Deep Learning Methods for Target Detection and Recognition (necessary for entry and improvement)
[Target Detection & Recognition] Dry goods | Video salient target detection (full source code is attached at the end of the article)
[Target Detection & Recognition] The performance has been greatly improved (speed & occlusion) | Basic knowledge of target detection based on area decomposition & integration | Knowledge and understanding of Anchor in target detection
[Target Detection & Recognition] Basic Knowledge | Knowledge and Understanding of Anchor in Target Detection
[Target Detection & Recognition] Three-branch network - the best network framework for target detection performance at present
[Target Detection & Recognition] Real-time target detection based on mobile phone system
[Target Detection & Recognition] Simple target detection and analysis
Face Detection & Recognition
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[Face] Currently the most powerful face detection algorithm (Wider Face Dataset)
【Face】Face field technology of future artificial intelligence
【Face】Recent Empirical Research on Face Alignment
[Face] Interpretation of GBDT (ERT) Algorithm for Face Alignment
[Face] Face Attention Mechanism Network
[Face] Robust Face Recognition for Effective Occlusion Detection
[Face] Discriminative feature learning method for face recognition
[Face] Face recognition - a new realm (unconstrained)
[Face] Trend and analysis of face detection and recognition (enhanced version)
[Face] Face detection and recognition technology (how to innovate?)
[Face] The Consistency of Advanced Face Recognition Technology in the Police Field
[Face] Trends and Analysis of Face Detection and Recognition
[Face] Face Alignment and Pose Standardization Based on Depth Model
[Face] Summary of Face Detection and Recognition
[Face] From face recognition to pedestrian re-recognition, the next outlet
[Face] Improved Shadow Suppression for Illumination Robust Face Recognition
[Face] Scale Invariant Face Detector (S3FD-Single Shot Scale-invariant Face Detector)
[Face] Powerful pose-aware model for pose-invariant face recognition
【Face】Face Special Collection 3 | Face key point detection (below) - source code at the end of the article
[Face] Face Collection 4 | Face key point detection based on factors such as occlusion and illumination
【Face】Face Collection 5 | The latest image quality evaluation
【Face】Phase Summary of Face Collection
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ABOUT
Institute of Computer Vision
The Institute of Computer Vision is mainly involved in the field of deep learning, and is mainly committed to research directions such as face detection, face recognition, multi-target detection, target tracking, and image segmentation. The research institute will continue to share the latest new paper algorithm framework. The difference in our reform this time is that we need to focus on "research". Afterwards, we will share the practical process for the corresponding fields, so that everyone can truly experience the real scene of getting rid of the theory, and cultivate the habit of loving programming and brain thinking!
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