Computer Vision and Its Implications Based on the Paper Imagenet Classification with Deep Convolutional Neural Networks by Alex Krizhevsky, Ilya Sutskever, Geoffrey E
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Synthesizing Images of Humans in Unseen Poses
Synthesizing Images of Humans in Unseen Poses Guha Balakrishnan Amy Zhao Adrian V. Dalca Fredo Durand MIT MIT MIT and MGH MIT [email protected] [email protected] [email protected] [email protected] John Guttag MIT [email protected] Abstract We address the computational problem of novel human pose synthesis. Given an image of a person and a desired pose, we produce a depiction of that person in that pose, re- taining the appearance of both the person and background. We present a modular generative neural network that syn- Source Image Target Pose Synthesized Image thesizes unseen poses using training pairs of images and poses taken from human action videos. Our network sepa- Figure 1. Our method takes an input image along with a desired target pose, and automatically synthesizes a new image depicting rates a scene into different body part and background lay- the person in that pose. We retain the person’s appearance as well ers, moves body parts to new locations and refines their as filling in appropriate background textures. appearances, and composites the new foreground with a hole-filled background. These subtasks, implemented with separate modules, are trained jointly using only a single part details consistent with the new pose. Differences in target image as a supervised label. We use an adversarial poses can cause complex changes in the image space, in- discriminator to force our network to synthesize realistic volving several moving parts and self-occlusions. Subtle details conditioned on pose. We demonstrate image syn- details such as shading and edges should perceptually agree thesis results on three action classes: golf, yoga/workouts with the body’s configuration. -
Memristor-Based Approximated Computation
Memristor-based Approximated Computation Boxun Li1, Yi Shan1, Miao Hu2, Yu Wang1, Yiran Chen2, Huazhong Yang1 1Dept. of E.E., TNList, Tsinghua University, Beijing, China 2Dept. of E.C.E., University of Pittsburgh, Pittsburgh, USA 1 Email: [email protected] Abstract—The cessation of Moore’s Law has limited further architectures, which not only provide a promising hardware improvements in power efficiency. In recent years, the physical solution to neuromorphic system but also help drastically close realization of the memristor has demonstrated a promising the gap of power efficiency between computing systems and solution to ultra-integrated hardware realization of neural net- works, which can be leveraged for better performance and the brain. The memristor is one of those promising devices. power efficiency gains. In this work, we introduce a power The memristor is able to support a large number of signal efficient framework for approximated computations by taking connections within a small footprint by taking the advantage advantage of the memristor-based multilayer neural networks. of the ultra-integration density [7]. And most importantly, A programmable memristor approximated computation unit the nonvolatile feature that the state of the memristor could (Memristor ACU) is introduced first to accelerate approximated computation and a memristor-based approximated computation be tuned by the current passing through itself makes the framework with scalability is proposed on top of the Memristor memristor a potential, perhaps even the best, device to realize ACU. We also introduce a parameter configuration algorithm of neuromorphic computing systems with picojoule level energy the Memristor ACU and a feedback state tuning circuit to pro- consumption [8], [9]. -
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design Jeffrey Dean Google Research [email protected] Abstract The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks. This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build, especially in the post-Moore’s Law-era. It also discusses some of the ways that machine learning may also be able to help with some aspects of the circuit design process. Finally, it provides a sketch of at least one interesting direction towards much larger-scale multi-task models that are sparsely activated and employ much more dynamic, example- and task-based routing than the machine learning models of today. Introduction The past decade has seen a remarkable series of advances in machine learning (ML), and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas [LeCun et al. 2015]. Major areas of significant advances include computer vision [Krizhevsky et al. 2012, Szegedy et al. 2015, He et al. 2016, Real et al. 2017, Tan and Le 2019], speech recognition [Hinton et al. -
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
Accepted March 22, 2020 Digital Object Identifier 10.1109/ACCESS.2020.2983149 A Survey of Autonomous Driving: Common Practices and Emerging Technologies EKIM YURTSEVER1, (Member, IEEE), JACOB LAMBERT 1, ALEXANDER CARBALLO 1, (Member, IEEE), AND KAZUYA TAKEDA 1, 2, (Senior Member, IEEE) 1Nagoya University, Furo-cho, Nagoya, 464-8603, Japan 2Tier4 Inc. Nagoya, Japan Corresponding author: Ekim Yurtsever (e-mail: [email protected]). ABSTRACT Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high- level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state- of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development. INDEX TERMS Autonomous Vehicles, Control, Robotics, Automation, Intelligent Vehicles, Intelligent Transportation Systems I. INTRODUCTION necessary here. CCORDING to a recent technical report by the Eureka Project PROMETHEUS [11] was carried out in A National Highway Traffic Safety Administration Europe between 1987-1995, and it was one of the earliest (NHTSA), 94% of road accidents are caused by human major automated driving studies. The project led to the errors [1]. Against this backdrop, Automated Driving Sys- development of VITA II by Daimler-Benz, which succeeded tems (ADSs) are being developed with the promise of in automatically driving on highways [12]. -
CSE 152: Computer Vision Manmohan Chandraker
CSE 152: Computer Vision Manmohan Chandraker Lecture 15: Optimization in CNNs Recap Engineered against learned features Label Convolutional filters are trained in a Dense supervised manner by back-propagating classification error Dense Dense Convolution + pool Label Convolution + pool Classifier Convolution + pool Pooling Convolution + pool Feature extraction Convolution + pool Image Image Jia-Bin Huang and Derek Hoiem, UIUC Two-layer perceptron network Slide credit: Pieter Abeel and Dan Klein Neural networks Non-linearity Activation functions Multi-layer neural network From fully connected to convolutional networks next layer image Convolutional layer Slide: Lazebnik Spatial filtering is convolution Convolutional Neural Networks [Slides credit: Efstratios Gavves] 2D spatial filters Filters over the whole image Weight sharing Insight: Images have similar features at various spatial locations! Key operations in a CNN Feature maps Spatial pooling Non-linearity Convolution (Learned) . Input Image Input Feature Map Source: R. Fergus, Y. LeCun Slide: Lazebnik Convolution as a feature extractor Key operations in a CNN Feature maps Rectified Linear Unit (ReLU) Spatial pooling Non-linearity Convolution (Learned) Input Image Source: R. Fergus, Y. LeCun Slide: Lazebnik Key operations in a CNN Feature maps Spatial pooling Max Non-linearity Convolution (Learned) Input Image Source: R. Fergus, Y. LeCun Slide: Lazebnik Pooling operations • Aggregate multiple values into a single value • Invariance to small transformations • Keep only most important information for next layer • Reduces the size of the next layer • Fewer parameters, faster computations • Observe larger receptive field in next layer • Hierarchically extract more abstract features Key operations in a CNN Feature maps Spatial pooling Non-linearity Convolution (Learned) . Input Image Input Feature Map Source: R. -
1 Convolution
CS1114 Section 6: Convolution February 27th, 2013 1 Convolution Convolution is an important operation in signal and image processing. Convolution op- erates on two signals (in 1D) or two images (in 2D): you can think of one as the \input" signal (or image), and the other (called the kernel) as a “filter” on the input image, pro- ducing an output image (so convolution takes two images as input and produces a third as output). Convolution is an incredibly important concept in many areas of math and engineering (including computer vision, as we'll see later). Definition. Let's start with 1D convolution (a 1D \image," is also known as a signal, and can be represented by a regular 1D vector in Matlab). Let's call our input vector f and our kernel g, and say that f has length n, and g has length m. The convolution f ∗ g of f and g is defined as: m X (f ∗ g)(i) = g(j) · f(i − j + m=2) j=1 One way to think of this operation is that we're sliding the kernel over the input image. For each position of the kernel, we multiply the overlapping values of the kernel and image together, and add up the results. This sum of products will be the value of the output image at the point in the input image where the kernel is centered. Let's look at a simple example. Suppose our input 1D image is: f = 10 50 60 10 20 40 30 and our kernel is: g = 1=3 1=3 1=3 Let's call the output image h. -
Lecture 1: Introduction
Lecture 1: Introduction Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 1 - 1 4/4/2017 Welcome to CS231n Top row, left to right: Middle row, left to right Bottom row, left to right Image by Augustas Didžgalvis; licensed under CC BY-SA 3.0; changes made Image by BGPHP Conference is licensed under CC BY 2.0; changes made Image is CC0 1.0 public domain Image by Nesster is licensed under CC BY-SA 2.0 Image is CC0 1.0 public domain Image by Derek Keats is licensed under CC BY 2.0; changes made Image is CC0 1.0 public domain Image by NASA is licensed under CC BY 2.0; chang Image is public domain Image is CC0 1.0 public domain Image is CC0 1.0 public domain Image by Ted Eytan is licensed under CC BY-SA 2.0; changes made Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 1 - 2 4/4/2017 Biology Psychology Neuroscience Physics optics Cognitive sciences Image graphics, algorithms, processing Computer theory,… Computer Science Vision systems, Speech, NLP architecture, … Robotics Information retrieval Engineering Machine learning Mathematics Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 1 - 3 4/4/2017 Biology Psychology Neuroscience Physics optics Cognitive sciences Image graphics, algorithms, processing Computer theory,… Computer Science Vision systems, Speech, NLP architecture, … Robotics Information retrieval Engineering Machine learning Mathematics Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 1 - 4 4/4/2017 Related Courses @ Stanford • CS131 (Fall 2016, Profs. Fei-Fei Li & Juan Carlos Niebles): – Undergraduate introductory class • CS 224n (Winter 2017, Prof. -
Self-Driving Cars: Evaluation of Deep Learning Techniques for Object Detection in Different Driving Conditions Ramesh Simhambhatla SMU, [email protected]
SMU Data Science Review Volume 2 | Number 1 Article 23 2019 Self-Driving Cars: Evaluation of Deep Learning Techniques for Object Detection in Different Driving Conditions Ramesh Simhambhatla SMU, [email protected] Kevin Okiah SMU, [email protected] Shravan Kuchkula SMU, [email protected] Robert Slater Southern Methodist University, [email protected] Follow this and additional works at: https://scholar.smu.edu/datasciencereview Part of the Artificial Intelligence and Robotics Commons, Other Computer Engineering Commons, and the Statistical Models Commons Recommended Citation Simhambhatla, Ramesh; Okiah, Kevin; Kuchkula, Shravan; and Slater, Robert (2019) "Self-Driving Cars: Evaluation of Deep Learning Techniques for Object Detection in Different Driving Conditions," SMU Data Science Review: Vol. 2 : No. 1 , Article 23. Available at: https://scholar.smu.edu/datasciencereview/vol2/iss1/23 This Article is brought to you for free and open access by SMU Scholar. It has been accepted for inclusion in SMU Data Science Review by an authorized administrator of SMU Scholar. For more information, please visit http://digitalrepository.smu.edu. Simhambhatla et al.: Self-Driving Cars: Evaluation of Deep Learning Techniques for Object Detection in Different Driving Conditions Self-Driving Cars: Evaluation of Deep Learning Techniques for Object Detection in Different Driving Conditions Kevin Okiah1, Shravan Kuchkula1, Ramesh Simhambhatla1, Robert Slater1 1 Master of Science in Data Science, Southern Methodist University, Dallas, TX 75275 USA {KOkiah, SKuchkula, RSimhambhatla, RSlater}@smu.edu Abstract. Deep Learning has revolutionized Computer Vision, and it is the core technology behind capabilities of a self-driving car. Convolutional Neural Networks (CNNs) are at the heart of this deep learning revolution for improving the task of object detection. -
Lecture Notes Geoffrey Hinton
Lecture Notes Geoffrey Hinton Overjoyed Luce crops vectorially. Tailor write-ups his glasshouse divulgating unmanly or constructively after Marcellus barb and outdriven squeakingly, diminishable and cespitose. Phlegmatical Laurance contort thereupon while Bruce always dimidiating his melancholiac depresses what, he shores so finitely. For health care about working memory networks are the class and geoffrey hinton and modify or are A practical guide to training restricted boltzmann machines Lecture Notes in. Trajectory automatically learn about different domains, geoffrey explained what a lecture notes geoffrey hinton notes was central bottleneck form of data should take much like a single example, geoffrey hinton with mctsnets. Gregor sieber and password you may need to this course that models decisions. Jimmy Ba Geoffrey Hinton Volodymyr Mnih Joel Z Leibo and Catalin Ionescu. YouTube lectures by Geoffrey Hinton3 1 Introduction In this topic of boosting we combined several simple classifiers into very complex classifier. Separating Figure from stand with a Parallel Network Paul. But additionally has efficient. But everett also look up. You already know how the course that is just to my assignment in the page and writer recognition and trends in the effect of the motivating this. These perplex the or free liquid Intelligence educational. Citation to lose sight of language. Sparse autoencoder CS294A Lecture notes Andrew Ng Stanford University. Geoffrey Hinton on what's nothing with CNNs Daniel C Elton. Toronto Geoffrey Hinton Advanced Machine Learning CSC2535 2011 Spring. Cross validated is sparse, a proof of how to list of possible configurations have to see. Cnns that just download a computational power nor the squared error in permanent electrode array with respect to the. -
What's the Point: Semantic Segmentation with Point Supervision
What's the Point: Semantic Segmentation with Point Supervision Amy Bearman1, Olga Russakovsky2, Vittorio Ferrari3, and Li Fei-Fei1 1 Stanford University fabearman,[email protected] 2 Carnegie Mellon University [email protected] 3 University of Edinburgh [email protected] Abstract. The semantic image segmentation task presents a trade-off between test time accuracy and training-time annotation cost. Detailed per-pixel annotations enable training accurate models but are very time- consuming to obtain; image-level class labels are an order of magnitude cheaper but result in less accurate models. We take a natural step from image-level annotation towards stronger supervision: we ask annotators to point to an object if one exists. We incorporate this point supervision along with a novel objectness potential in the training loss function of a CNN model. Experimental results on the PASCAL VOC 2012 benchmark reveal that the combined effect of point-level supervision and object- ness potential yields an improvement of 12:9% mIOU over image-level supervision. Further, we demonstrate that models trained with point- level supervision are more accurate than models trained with image-level, squiggle-level or full supervision given a fixed annotation budget. Keywords: semantic segmentation, weak supervision, data annotation 1 Introduction At the forefront of visual recognition is the question of how to effectively teach computers new concepts. Algorithms trained from carefully annotated data enjoy better performance than their weakly supervised counterparts (e.g., [1] vs. [2], [3] vs. [4], [5] vs. [6]), yet obtaining such data is very time-consuming [5, 7]. It is particularly difficult to collect training data for semantic segmentation, i.e., the task of assigning a class label to every pixel in the image. -
Do Better Imagenet Models Transfer Better?
Do Better ImageNet Models Transfer Better? Simon Kornblith,∗ Jonathon Shlens, and Quoc V. Le Google Brain {skornblith,shlens,qvl}@google.com Logistic Regression Fine-Tuned Abstract 1.6 2.3 Inception-ResNet v2 Inception v4 1.5 2.2 Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship 1.4 MobileNet v1 2.1 MobileNet v1 NASNet Large NASNet Large between architecture and transfer. An implicit hypothesis 1.3 2.0 ResNet-50 ResNet-50 in modern computer vision research is that models that per- 1.2 1.9 form better on ImageNet necessarily perform better on other vision tasks. However, this hypothesis has never been sys- 1.1 1.8 Transfer Accuracy (Log Odds) tematically tested. Here, we compare the performance of 16 72 74 76 78 80 72 74 76 78 80 classification networks on 12 image classification datasets. ImageNet Top-1 Accuracy (%) ImageNet Top-1 Accuracy (%) We find that, when networks are used as fixed feature ex- Figure 1. Transfer learning performance is highly correlated with tractors or fine-tuned, there is a strong correlation between ImageNet top-1 accuracy for fixed ImageNet features (left) and ImageNet accuracy and transfer accuracy (r = 0.99 and fine-tuning from ImageNet initialization (right). The 16 points in 0.96, respectively). In the former setting, we find that this re- each plot represent transfer accuracy for 16 distinct CNN architec- tures, averaged across 12 datasets after logit transformation (see lationship is very sensitive to the way in which networks are Section 3). -
Feature Mining: a Novel Training Strategy for Convolutional Neural
Feature Mining: A Novel Training Strategy for Convolutional Neural Network Tianshu Xie1 Xuan Cheng1 Xiaomin Wang1 [email protected] [email protected] [email protected] Minghui Liu1 Jiali Deng1 Ming Liu1* [email protected] [email protected] [email protected] Abstract In this paper, we propose a novel training strategy for convo- lutional neural network(CNN) named Feature Mining, that aims to strengthen the network’s learning of the local feature. Through experiments, we find that semantic contained in different parts of the feature is different, while the network will inevitably lose the local information during feedforward propagation. In order to enhance the learning of local feature, Feature Mining divides the complete feature into two com- plementary parts and reuse these divided feature to make the network learn more local information, we call the two steps as feature segmentation and feature reusing. Feature Mining is a parameter-free method and has plug-and-play nature, and can be applied to any CNN models. Extensive experiments demonstrate the wide applicability, versatility, and compatibility of our method. 1 Introduction Figure 1. Illustration of feature segmentation used in our method. In each iteration, we use a random binary mask to Convolution neural network (CNN) has made significant divide the feature into two parts. progress on various computer vision tasks, 4.6, image classi- fication10 [ , 17, 23, 25], object detection [7, 9, 22], and seg- mentation [3, 19]. However, the large scale and tremendous parameters of CNN may incur overfitting and reduce gener- training strategy for CNN for strengthening the network’s alizations, that bring challenges to the network training.