For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-leontiev
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Anton Leontiev, Embedded Software Architect at ELVEES, JSC, presents the "Designing a Stereo IP Camera From Scratch" tutorial at the May 2017 Embedded Vision Summit.
As the number of cameras in an intelligent video surveillance system increases, server processing of the video quickly becomes a bottleneck. On the other hand, when computer vision algorithms are moved to a resource-limited camera platform, their output quality is often unsatisfactory.
The effectiveness of vision algorithms for surveillance can be greatly improved by using a depth map in addition to the regular image. Thus, using a stereo camera is a way to enable offloading of advanced algorithms from servers to IP cameras. This talk covers the main problems arising during the design of an embedded stereo IP camera, including capturing video streams from two sensors, frame synchronization between sensors, stereo calibration algorithms, and, finally, disparity map calculation.
Extend Your Journey: Introducing Signal Strength into Location-based Applicat...Chih-Chuan Cheng
Reducing the communication energy is essential to facilitate the growth of emerging mobile applications. In this paper, we introduce signal strength into location-based applications to reduce the energy consumption of mobile devices for data reception. First, we model the problem of data fetch scheduling, with the objective of minimizing the energy required to fetch location-based information without adversely impacting user experience. Then, we propose a dynamic-programming algorithm to solve the fundamental problem and prove its optimality in terms of energy savings. We also provide an optimality condition with respect to signal strength fluctuations. Finally, based on the algorithm, we consider implementation issues. We have also developed a virtual tour system integrated with existing web applications to validate the practicability of the proposed concept. The results of experiments conducted based on real-world case studies are very encouraging.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/introduction-to-simultaneous-localization-and-mapping-slam-a-presentation-from-gareth-cross/
Independent game developer (and former technical lead of state estimation at Skydio) Gareth Cross presents the “Introduction to Simultaneous Localization and Mapping (SLAM)” tutorial at the May 2021 Embedded Vision Summit.
This talk provides an introduction to the fundamentals of simultaneous localization and mapping (SLAM). Cross aims to provide foundational knowledge, and viewers are not expected to have any prerequisite experience in the field.
The talk consists of an introduction to the concept of SLAM, as well as practical design considerations in formulating SLAM problems. Visual inertial odometry is introduced as a motivating example of SLAM, and Cross explains how this problem is structured and solved.
Scratch to Supercomputers: Bottoms-up Build of Large-scale Computational Lens...inside-BigData.com
In this deck from the 2018 Swiss HPC Conference, Gilles Fourestey from EPFL presents: Scratch to Supercomputers: Bottoms-up Build of Large-scale Computational Lensing Software.
"LENSTOOL is a gravitational lensing software that models mass distribution of galaxies and clusters. It was developed by Prof. Kneib, head of the LASTRO lab at EPFL, et al., starting from 1996. It is used to obtain sub-percent precision measurements of the total mass in galaxy clusters and constrain the dark matter self-interaction cross-section, a crucial ingredient to understanding its nature.
However, LENSTOOL lacks efficient vectorization and only uses OpenMP, which limits its execution to one node and can lead to execution times that exceed several months. Therefore, the LASTRO and the EPFL HPC group decided to rewrite the code from scratch and in order to minimize risk and maximize performance, a bottom-up approach that focuses on exposing parallelism at hardware and instruction levels was used. The result is a high performance code, fully vectorized on Xeon, Xeon Phis and GPUs that currently scales up to hundreds of nodes on CSCS’ Piz Daint, one of the fastest supercomputers in the world."
Watch the video: https://wp.me/p3RLHQ-ili
Learn more: https://infoscience.epfl.ch/record/234382/files/EPFL_TH8338.pdf?subformat=pdfa
and
http://www.hpcadvisorycouncil.com/events/2018/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-kim
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Minyoung Kim, Senior Research Engineer at Panasonic Silicon Valley Laboratory, presents the "A Fast Object Detector for ADAS using Deep Learning" tutorial at the May 2017 Embedded Vision Summit.
Object detection has been one of the most important research areas in computer vision for decades. Recently, deep neural networks (DNNs) have led to significant improvement in several machine learning domains, including computer vision, achieving the state-of-the-art performance thanks to their theoretically proven modeling and generalization capabilities. However, it is still challenging to deploy such DNNs on embedded systems, for applications such as advanced driver assistance systems (ADAS), where computation power is limited.
Kim and her team focus on reducing the size of the network and required computations, and thus building a fast, real-time object detection system. They propose a fully convolutional neural network that can achieve at least 45 fps on 640x480 frames with competitive performance. With this network, there is no proposal generation step, which can cause a speed bottleneck; instead, a single forward propagation of the network approximates the locations of objects directly.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/efficient-deep-learning-for-3d-point-cloud-understanding-a-presentation-from-facebook/
Bichen Wu, Research Scientist at Facebook Reality Labs, presents the “Efficient Deep Learning for 3D Point Cloud Understanding” tutorial at the May 2021 Embedded Vision Summit.
Understanding the 3D environment is a crucial computer vision capability required by a growing set of applications such as autonomous driving, AR/VR and AIoT. 3D visual information, captured by LiDAR and other sensors, is typically represented by a point cloud consisting of thousands of unstructured points.
Developing computer vision solutions to understand 3D point clouds requires addressing several challenges, including how to efficiently represent and process 3D point clouds, how to design efficient on-device neural networks to process 3D point clouds, and how to easily obtain data to train 3D models and improve data efficiency. In this talk, Wu shows how his company addresses these challenges as part of its “SqeezeSeg” research and presents a highly efficient, accurate, and data-efficient solution for on-device 3D point-cloud understanding.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-leontiev
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Anton Leontiev, Embedded Software Architect at ELVEES, JSC, presents the "Designing a Stereo IP Camera From Scratch" tutorial at the May 2017 Embedded Vision Summit.
As the number of cameras in an intelligent video surveillance system increases, server processing of the video quickly becomes a bottleneck. On the other hand, when computer vision algorithms are moved to a resource-limited camera platform, their output quality is often unsatisfactory.
The effectiveness of vision algorithms for surveillance can be greatly improved by using a depth map in addition to the regular image. Thus, using a stereo camera is a way to enable offloading of advanced algorithms from servers to IP cameras. This talk covers the main problems arising during the design of an embedded stereo IP camera, including capturing video streams from two sensors, frame synchronization between sensors, stereo calibration algorithms, and, finally, disparity map calculation.
Extend Your Journey: Introducing Signal Strength into Location-based Applicat...Chih-Chuan Cheng
Reducing the communication energy is essential to facilitate the growth of emerging mobile applications. In this paper, we introduce signal strength into location-based applications to reduce the energy consumption of mobile devices for data reception. First, we model the problem of data fetch scheduling, with the objective of minimizing the energy required to fetch location-based information without adversely impacting user experience. Then, we propose a dynamic-programming algorithm to solve the fundamental problem and prove its optimality in terms of energy savings. We also provide an optimality condition with respect to signal strength fluctuations. Finally, based on the algorithm, we consider implementation issues. We have also developed a virtual tour system integrated with existing web applications to validate the practicability of the proposed concept. The results of experiments conducted based on real-world case studies are very encouraging.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/introduction-to-simultaneous-localization-and-mapping-slam-a-presentation-from-gareth-cross/
Independent game developer (and former technical lead of state estimation at Skydio) Gareth Cross presents the “Introduction to Simultaneous Localization and Mapping (SLAM)” tutorial at the May 2021 Embedded Vision Summit.
This talk provides an introduction to the fundamentals of simultaneous localization and mapping (SLAM). Cross aims to provide foundational knowledge, and viewers are not expected to have any prerequisite experience in the field.
The talk consists of an introduction to the concept of SLAM, as well as practical design considerations in formulating SLAM problems. Visual inertial odometry is introduced as a motivating example of SLAM, and Cross explains how this problem is structured and solved.
Scratch to Supercomputers: Bottoms-up Build of Large-scale Computational Lens...inside-BigData.com
In this deck from the 2018 Swiss HPC Conference, Gilles Fourestey from EPFL presents: Scratch to Supercomputers: Bottoms-up Build of Large-scale Computational Lensing Software.
"LENSTOOL is a gravitational lensing software that models mass distribution of galaxies and clusters. It was developed by Prof. Kneib, head of the LASTRO lab at EPFL, et al., starting from 1996. It is used to obtain sub-percent precision measurements of the total mass in galaxy clusters and constrain the dark matter self-interaction cross-section, a crucial ingredient to understanding its nature.
However, LENSTOOL lacks efficient vectorization and only uses OpenMP, which limits its execution to one node and can lead to execution times that exceed several months. Therefore, the LASTRO and the EPFL HPC group decided to rewrite the code from scratch and in order to minimize risk and maximize performance, a bottom-up approach that focuses on exposing parallelism at hardware and instruction levels was used. The result is a high performance code, fully vectorized on Xeon, Xeon Phis and GPUs that currently scales up to hundreds of nodes on CSCS’ Piz Daint, one of the fastest supercomputers in the world."
Watch the video: https://wp.me/p3RLHQ-ili
Learn more: https://infoscience.epfl.ch/record/234382/files/EPFL_TH8338.pdf?subformat=pdfa
and
http://www.hpcadvisorycouncil.com/events/2018/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-kim
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Minyoung Kim, Senior Research Engineer at Panasonic Silicon Valley Laboratory, presents the "A Fast Object Detector for ADAS using Deep Learning" tutorial at the May 2017 Embedded Vision Summit.
Object detection has been one of the most important research areas in computer vision for decades. Recently, deep neural networks (DNNs) have led to significant improvement in several machine learning domains, including computer vision, achieving the state-of-the-art performance thanks to their theoretically proven modeling and generalization capabilities. However, it is still challenging to deploy such DNNs on embedded systems, for applications such as advanced driver assistance systems (ADAS), where computation power is limited.
Kim and her team focus on reducing the size of the network and required computations, and thus building a fast, real-time object detection system. They propose a fully convolutional neural network that can achieve at least 45 fps on 640x480 frames with competitive performance. With this network, there is no proposal generation step, which can cause a speed bottleneck; instead, a single forward propagation of the network approximates the locations of objects directly.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/efficient-deep-learning-for-3d-point-cloud-understanding-a-presentation-from-facebook/
Bichen Wu, Research Scientist at Facebook Reality Labs, presents the “Efficient Deep Learning for 3D Point Cloud Understanding” tutorial at the May 2021 Embedded Vision Summit.
Understanding the 3D environment is a crucial computer vision capability required by a growing set of applications such as autonomous driving, AR/VR and AIoT. 3D visual information, captured by LiDAR and other sensors, is typically represented by a point cloud consisting of thousands of unstructured points.
Developing computer vision solutions to understand 3D point clouds requires addressing several challenges, including how to efficiently represent and process 3D point clouds, how to design efficient on-device neural networks to process 3D point clouds, and how to easily obtain data to train 3D models and improve data efficiency. In this talk, Wu shows how his company addresses these challenges as part of its “SqeezeSeg” research and presents a highly efficient, accurate, and data-efficient solution for on-device 3D point-cloud understanding.
Intelligent Image Enhancement and Restoration - From Prior Driven Model to Ad...Wanjin Yu
ICME2019 Tutorial: Intelligent Image Enhancement and Restoration - From Prior Driven Model to Advanced Deep Learning Part 3: prior embedding deep super resolution
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/an-introduction-to-single-photon-avalanche-diodes-a-new-type-of-imager-for-computer-vision-a-presentation-from-the-university-of-wisconsin-madison/
Sebastian Bauer, Postdoctoral Student at the University of Wisconsin – Madison, presents the “Introduction to Single-Photon Avalanche Diodes—A New Type of Imager for Computer Vision” tutorial at the May 2021 Embedded Vision Summit.
The single-photon avalanche diode (SPAD) is an emerging image sensing technology with unique capabilities relevant to computer vision applications. Originally designed for imaging in low-light conditions, the ultra-high time resolution of SPADs also helps to achieve extremely high dynamic range, motion blur-free images and even seeing around corners. The use of SPADs in recent iPhone models has spurred increased interest in the use of SPADs in commercial products.
In this talk, Bauer introduces SPAD-based imagers, explains how they work, presents their fundamental capabilities, and identifies their key strengths and weaknesses relative to conventional image sensors. He also shows how they can be used in a variety of applications.
Convolutional Neural Networks for Image Classification (Cape Town Deep Learni...Alex Conway
Slides for my talk on:
"Convolutional Neural Networks for Image Classification"
...at the Cape Town Deep Learning Meet-up 20170620
https://www.meetup.com/Cape-Town-deep-learning/events/240485642/
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2018-embedded-vision-summit-benosman
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Ryad B. Benosman, Professor at the University of Pittsburgh Medical Center, Carnegie Mellon University and Sorbonne Universitas, presents the "What is Neuromorphic Event-based Computer Vision? Sensors, Theory and Applications" tutorial at the May 2018 Embedded Vision Summit.
In this presentation, Benosman introduces neuromorphic, event-based approaches for image sensing and processing. State-of-the-art image sensors suffer from severe limitations imposed by their very principle of operation. These sensors acquire the visual information as a series of “snapshots” recorded at discrete point in time, hence time-quantized at a predetermined frame rate, resulting in limited temporal resolution, low dynamic range and a high degree of redundancy in the acquired data. Nature suggests a different approach: Biological vision systems are driven and controlled by events happening within the scene in view, and not – like conventional image sensors – by artificially created timing and control signals that have no relation to the source of the visual information.
Translating the frameless paradigm of biological vision to artificial imaging systems implies that control over the acquisition of visual information is no longer imposed externally on an array of pixels but rather the decision making is transferred to each individual pixel, which handles its own information individually. Benosman introduces the fundamentals underlying such bio-inspired, event-based image sensing and processing approaches, and explores their strengths and weaknesses. He shows that bio-inspired vision systems have the potential to outperform conventional, frame-based vision acquisition and processing systems and to establish new benchmarks in terms of data compression, dynamic range, temporal resolution and power efficiency in applications such as 3D vision, object tracking, motor control and visual feedback loops, in real-time.
This presentation was for my Honours project proposal. Presented to a chair of lecturers and peers.
It outlines the problem I aimed to tackle and the issues that I had discovered during research.
Intelligent Image Enhancement and Restoration - From Prior Driven Model to Ad...Wanjin Yu
ICME2019 Tutorial: Intelligent Image Enhancement and Restoration - From Prior Driven Model to Advanced Deep Learning Part 3: prior embedding deep super resolution
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/an-introduction-to-single-photon-avalanche-diodes-a-new-type-of-imager-for-computer-vision-a-presentation-from-the-university-of-wisconsin-madison/
Sebastian Bauer, Postdoctoral Student at the University of Wisconsin – Madison, presents the “Introduction to Single-Photon Avalanche Diodes—A New Type of Imager for Computer Vision” tutorial at the May 2021 Embedded Vision Summit.
The single-photon avalanche diode (SPAD) is an emerging image sensing technology with unique capabilities relevant to computer vision applications. Originally designed for imaging in low-light conditions, the ultra-high time resolution of SPADs also helps to achieve extremely high dynamic range, motion blur-free images and even seeing around corners. The use of SPADs in recent iPhone models has spurred increased interest in the use of SPADs in commercial products.
In this talk, Bauer introduces SPAD-based imagers, explains how they work, presents their fundamental capabilities, and identifies their key strengths and weaknesses relative to conventional image sensors. He also shows how they can be used in a variety of applications.
Convolutional Neural Networks for Image Classification (Cape Town Deep Learni...Alex Conway
Slides for my talk on:
"Convolutional Neural Networks for Image Classification"
...at the Cape Town Deep Learning Meet-up 20170620
https://www.meetup.com/Cape-Town-deep-learning/events/240485642/
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2018-embedded-vision-summit-benosman
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Ryad B. Benosman, Professor at the University of Pittsburgh Medical Center, Carnegie Mellon University and Sorbonne Universitas, presents the "What is Neuromorphic Event-based Computer Vision? Sensors, Theory and Applications" tutorial at the May 2018 Embedded Vision Summit.
In this presentation, Benosman introduces neuromorphic, event-based approaches for image sensing and processing. State-of-the-art image sensors suffer from severe limitations imposed by their very principle of operation. These sensors acquire the visual information as a series of “snapshots” recorded at discrete point in time, hence time-quantized at a predetermined frame rate, resulting in limited temporal resolution, low dynamic range and a high degree of redundancy in the acquired data. Nature suggests a different approach: Biological vision systems are driven and controlled by events happening within the scene in view, and not – like conventional image sensors – by artificially created timing and control signals that have no relation to the source of the visual information.
Translating the frameless paradigm of biological vision to artificial imaging systems implies that control over the acquisition of visual information is no longer imposed externally on an array of pixels but rather the decision making is transferred to each individual pixel, which handles its own information individually. Benosman introduces the fundamentals underlying such bio-inspired, event-based image sensing and processing approaches, and explores their strengths and weaknesses. He shows that bio-inspired vision systems have the potential to outperform conventional, frame-based vision acquisition and processing systems and to establish new benchmarks in terms of data compression, dynamic range, temporal resolution and power efficiency in applications such as 3D vision, object tracking, motor control and visual feedback loops, in real-time.
This presentation was for my Honours project proposal. Presented to a chair of lecturers and peers.
It outlines the problem I aimed to tackle and the issues that I had discovered during research.
Recent Progress on Object Detection_20170331Jihong Kang
This slide provides a brief summary of recent progress on object detection using deep learning.
The concept of selected previous works(R-CNN series/YOLO/SSD) and 6 recent papers (uploaded to the Arxiv between Dec/2016 and Mar/2017) are introduced in this slide.
Most papers are focusing on improving the performance of small object detection.
Implementation of digital image watermarking techniques using dwt and dwt svd...eSAT Journals
computerized substance. Data took care of on web and mixed media system framework is in advanced structure. Computerized watermarking is only the innovation in which there is inserting of different data in advanced substance, which we need to shield from illicit replicating. Computerized picture watermarking is concealing data in any structure (content, picture, sound and video) in unique picture without corrupting its perceptual quality. On the off chance that of Discrete Wavelet Transform (DWT), deterioration of the first picture is completed to insert the watermark. Moreover, if there should arise an occurrence of cross breed system (DWT-SVD) firstly picture is decayed by and after that watermark is installed in solitary qualities acquired by application of Singular Value Decomposition (SVD). DWT and SVD are utilized in combination to enhance the nature of watermarking. We have the procedures which are looked at on the premise of Peak Signal to Noise Ratio (PSNR) esteem at various benefits of scaling component; high estimation of PSNR is coveted because it displays great intangibility of the strategy.
Implementation of digital image watermarking techniques using dwt and dwt svd...eSAT Journals
Abstract
These days, in every field there is gigantic utilization of computerized substance. Data took care of on web and mixed media system framework is in advanced structure. Computerized watermarking is only the innovation in which there is inserting of different data in advanced substance, which we need to shield from illicit replicating. Computerized picture watermarking is concealing data in any structure (content, picture, sound and video) in unique picture without corrupting its perceptual quality. On the off chance that of Discrete Wavelet Transform (DWT), deterioration of the first picture is completed to insert the watermark. Moreover, if there should arise an occurrence of cross breed system (DWT-SVD) firstly picture is decayed by and after that watermark is installed in solitary qualities acquired by application of Singular Value Decomposition (SVD). DWT and SVD are utilized in combination to enhance the nature of watermarking. We have the procedures which are looked at on the premise of Peak Signal to Noise Ratio (PSNR) esteem at various benefits of scaling component; high estimation of PSNR is coveted because it displays great intangibility of the strategy.
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and ArchitecturesMLAI2
Regularization and transfer learning are two popular techniques to enhance generalization on unseen data, which is a fundamental problem of machine learning. Regularization techniques are versatile, as they are task- and architecture-agnostic, but they do not exploit a large amount of data available. Transfer learning methods learn to transfer knowledge from one domain to another, but may not generalize across tasks and architectures, and may introduce new training cost for adapting to the target task. To bridge the gap between the two, we propose a transferable perturbation, MetaPerturb, which is meta-learned to improve generalization performance on unseen data. MetaPerturb is implemented as a set-based lightweight network that is agnostic to the size and the order of the input, which is shared across the layers. Then, we propose a meta-learning framework, to jointly train the perturbation function over heterogeneous tasks in parallel. As MetaPerturb is a set-function trained over diverse distributions across layers and tasks, it can generalize to heterogeneous tasks and architectures. We validate the efficacy and generality of MetaPerturb trained on a specific source domain and architecture, by applying it to the training of diverse neural architectures on heterogeneous target datasets against various regularizers and fine-tuning. The results show that the networks trained with MetaPerturb significantly outperform the baselines on most of the tasks and architectures, with a negligible increase in the parameter size and no hyperparameters to tune.
The 'Rubble of the North' -a solution for modelling the irregular architectur...3D ICONS Project
The 'Rubble of the North' -a solution for modelling the irregular architecture of Ireland's historic monuments - a presentation given by Rob Shaw of the Discovery Programme, Ireland at the 3D ICONS workshop at the ISPRS Technical Commission V Symposium, which was held in Riva del Garda, Italy on 23-25 June 2014.
The presentation gives and overview of the digitisation, the challenges faced, solutions and deliverables.
Structured Forests for Fast Edge Detection [Paper Presentation]Mohammad Shaker
A Paper Presentation for "Structured Forests for Fast Edge Detection" by Dollár, Piotr, and C. Lawrence Zitnick at Computer Vision (ICCV), 2013 IEEE International Conference on. IEEE, 2013.
Improving Hardware Efficiency for DNN ApplicationsChester Chen
Speaker: Dr. Hai (Helen) Li is the Clare Boothe Luce Associate Professor of Electrical and Computer Engineering and Co-director of the Duke Center for Evolutionary Intelligence at Duke University
In this talk, I will introduce a few recent research spotlights by the Duke Center for Evolutionary Intelligence. The talk will start with the structured sparsity learning (SSL) method which attempts to learn a compact structure from a bigger DNN to reduce computation cost. It generates a regularized structure with high execution efficiency. Our experiments on CPU, GPU, and FPGA platforms show on average 3~5 times speedup of convolutional layer computation of AlexNet. Then, the implementation and acceleration of DNN applications on mobile computing systems will be introduced. MoDNN is a local distributed system which partitions DNN models onto several mobile devices to accelerate computations. ApesNet is an efficient pixel-wise segmentation network, which understands road scenes in real-time, and has achieved promising accuracy. Our prospects on the adoption of emerging technology will also be given at the end of this talk, offering the audiences an alternative thinking about the future evolution and revolution of modern computing systems.
AUTO AI 2021 talk Real world data augmentations for autonomous driving : B Ra...Ravi Kiran B.
Modern perception pipelines in autonomous driving (AD) systems are based on Deep Neural Networks (DNNs) which utilize multiple hyper-parameter configurations and training strategies. Data augmentations is now a well-established training strategy to improve the generalization of DNNs, especially in a low dataset regime. Self-supervised learning and semi-supervised methods depend heavily on data augmentation strategies. In this study we view generalization due to data augmentations training DNNs since they implicitly model the geometric, viewpoint based transformations present on images/pointclouds due to noise, perspective, motion of the ego-vehicle. We shortly review current data augmentation strategies for perception tasks in AD, and recent developments on understanding its effects on model generalization.
In the talk we shall review data augmentation strategies through two case studies:
- Improving model performance of monocular 3D object detection model by using geometry preserving data augmentations on images
- Understand the role of data augmentation in reducing data redundancy and improving label efficiency within an active learning pipeline
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Similar to [AAAI2018] Multispectral Transfer Network: Unsupervised Depth Estimation for All-day Vision (20)
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Welocme to ViralQR, your best QR code generator.ViralQR
Welcome to ViralQR, your best QR code generator available on the market!
At ViralQR, we design static and dynamic QR codes. Our mission is to make business operations easier and customer engagement more powerful through the use of QR technology. Be it a small-scale business or a huge enterprise, our easy-to-use platform provides multiple choices that can be tailored according to your company's branding and marketing strategies.
Our Vision
We are here to make the process of creating QR codes easy and smooth, thus enhancing customer interaction and making business more fluid. We very strongly believe in the ability of QR codes to change the world for businesses in their interaction with customers and are set on making that technology accessible and usable far and wide.
Our Achievements
Ever since its inception, we have successfully served many clients by offering QR codes in their marketing, service delivery, and collection of feedback across various industries. Our platform has been recognized for its ease of use and amazing features, which helped a business to make QR codes.
Our Services
At ViralQR, here is a comprehensive suite of services that caters to your very needs:
Static QR Codes: Create free static QR codes. These QR codes are able to store significant information such as URLs, vCards, plain text, emails and SMS, Wi-Fi credentials, and Bitcoin addresses.
Dynamic QR codes: These also have all the advanced features but are subscription-based. They can directly link to PDF files, images, micro-landing pages, social accounts, review forms, business pages, and applications. In addition, they can be branded with CTAs, frames, patterns, colors, and logos to enhance your branding.
Pricing and Packages
Additionally, there is a 14-day free offer to ViralQR, which is an exceptional opportunity for new users to take a feel of this platform. One can easily subscribe from there and experience the full dynamic of using QR codes. The subscription plans are not only meant for business; they are priced very flexibly so that literally every business could afford to benefit from our service.
Why choose us?
ViralQR will provide services for marketing, advertising, catering, retail, and the like. The QR codes can be posted on fliers, packaging, merchandise, and banners, as well as to substitute for cash and cards in a restaurant or coffee shop. With QR codes integrated into your business, improve customer engagement and streamline operations.
Comprehensive Analytics
Subscribers of ViralQR receive detailed analytics and tracking tools in light of having a view of the core values of QR code performance. Our analytics dashboard shows aggregate views and unique views, as well as detailed information about each impression, including time, device, browser, and estimated location by city and country.
So, thank you for choosing ViralQR; we have an offer of nothing but the best in terms of QR code services to meet business diversity!
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
[AAAI2018] Multispectral Transfer Network: Unsupervised Depth Estimation for All-day Vision
1. Multispectral Transfer Network:
Unsupervised Depth Estimation for All-day Vision
AAAI 2018, New Orleans
Namil Kim*, Yukyung Choi*, Soonmin Hwang, In So Kweon
KAIST RCV Lab / All-day Vision Team
*Equal contributions
2. Problem definition
Why we are interesting in depth?
“Crucial information” to understand the world around us
*From NVidia
It is necessary to 3D understanding for self-decision making
3. Problem definition
How do we usually get “dense depth”
in any time of the day?
RGB-Stereo 3D LiDAR
DayNight
≤ 11.45m≥ 23.89m
4 points
2 points
LiDAR
0.16°
Sensitive Sparse
6. Idea to all-day depth estimation
Day Night
Illumination change
RGB
O X
Unsupervised
Learning
Unsupervised
Learning
7. Idea to all-day depth estimation
Day Night
Illumination change
RGBThermal
O X
Robust to illumination change
Unsupervised
Learning
Unsupervised
Learning
8. Idea to all-day depth estimation
Day Night
Illumination change
RGBThermal
Alignment
O X
Thermal-to-depth
#1
#2
Unsupervised
Learning
Unsupervised
Learning
9. Idea to all-day depth estimation
Day Night
Illumination change
RGBThermal
Alignment
O X
Thermal-to-depth
Adaptation
Robust to illumination change
Unsupervised
Learning
Unsupervised
Learning
10. Requirements #1
Multispectral (RGB-Thermal) dataset
RGB stereo pair
Alignment between thermal and RGB(left)
3D measurement
Yukyung Choi et al., KAIST Multispectral Recognition Dataset in Day and Night, TITS’18
11. Requirements #2
Multispectral (RGB-Thermal) Transfer Network
Aim: Thermal to depth prediction
Data: Thermal and aligned left RGB
(+ right RGB, stereo pair)
Model: unsupervised method
RGBThermal
Alignment
O
U.S.L
Thermal-to-depth
12. Proposed framework
What is Multispectral Transfer Network?
@Supervised method @Unsupervised method
@MTN method
14. Key Ideas of Proposed MTN (Overview)
1) Efficient Multi-task Learning
Predicting Depth, Surface Normals and Semantic Labels
with a Common Multi-Scale Convolutional Architecture,
ICCV2015.
Without annotated data:
Propose an efficient multi-task methodology
Depth and Chromaticity
- surface normal
- semantic labeling
- object pose annotation
* Most of works under an indoor.
(difficulty of collecting sources of
subsequent task in outdoor)
Multi-task learning for
depth estimation
No human-intensive data
Relevance to the depth
Contextual information
15. Key Ideas of Proposed MTN (1/4)
Predicting Depth, Surface Normals and Semantic Labels
with a Common Multi-Scale Convolutional Architecture,
ICCV2015.
- surface normal
- semantic labeling
- object pose annotation
* Most of works under an indoor.
(difficulty of collecting sources of
subsequent task in outdoor)
Previous works:
No human-intensive data
Relevance to the depth
Contextual information
Our work: Chromaticity
1) Efficient Multi-task Learning
Without annotated data:
Propose an efficient multi-task methodology
16. Key Ideas of Proposed MTN (2/4)
Interleaver Module:
to directly interleave the chromaticity into the depth estimation
“Skip-connection meets Inter-leaver for the feature learning”
Encoder Decoder
Multispectral Transfer Network (MTN)
2) Novel Module for Multi-task learning
Thermal Input
Disparity Output
Chromaticity Output
Conv.
DeConv.
Interleaver
Skip Connect.
Forward flow
17. Key Ideas of Proposed MTN (2/4)
2) Novel Module for Multi-task learning
1. Global/Un-Pooling + L2 Norm.
Enlarge receptive field [ParseNet] + feature transformation
2. Gating mechanism
Control the degree of the effectiveness of another task
to the main task. (especially in back-propagation).
3. Up-sampling and adding to previous output
Equipped in every skip-connected flows
(fully-connections between layers)
18. Key Ideas of Proposed MTN (2/4)
2) Novel Module for Multi-task learning
Do not have to find an optimal split point or
parameters. <c.f.,(b), (c), (d)>
Reduce adverse effects from inbuilt sharing
mechanism. <c.f.,(a), (b)>
Optimize the same strategy as the general multi-task
learning in end-to-end manner. <c.f., (d)>
In the inference, the Interleaver unit can be
removed. <c.f., (d)>
(a) Fully Shared Architecture
(c) No shared Architecture (d) Connected Architecture
(b) Partial Split Architectures
Previous Multi-task Learning Our Multi-task Learning
19. Key Ideas of Proposed MTN (3/4)
3) Photometric Correction
“Thermal Crossover”
Thermal-infrared image is not directly affected by changing lighting conditions.
However, thermal-infrared image suffers indirectly from cyclic illumination.
20. Key Ideas of Proposed MTN (4/4)
Propose the adaptive scaled sigmoid to stably train the
model as the bilinear activation function.
From the initial smaller maximum disparity 𝛽0,
we iteratively increase the value 𝛼 at each epoch
to cover the large disparity level in end of training.
According to the derivative,
this is not stable for large quantities in initial stages
4) Adaptive scaled sigmoid function
25. Conclusion
𝑰𝒏𝒕𝒆𝒓𝒍𝒆𝒂𝒗𝒆𝒓
in every skip-connected layer.
1. Pooling mechanism + L2 Norm.
(enlarge receptive field)
2. Gated Unit via Convolution
3. Up-sampling
Employ multi-task learning for depth estimation
Novel architecture for multi-task learning: Interleaver
Photometric correction is helpful to deal with a thermal image.
Adaptive sigmoid function help stable converge.