PR100: SeedNet: Automatic Seed Generation with Deep Reinforcement Learning for Robust Interactive Segmentation
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광희 이SeedNet: Automatic Seed Generation with Deep Reinforcement Learning for Robust Interactive Segmentation (CVPR2018)
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This document provides an introduction to computer vision. It summarizes the state of the field, including popular challenges like PASCAL VOC and SRVC. It describes commonly used algorithms like SIFT for feature extraction and bag-of-words models. It also discusses machine learning methods applied to computer vision like support vector machines, randomized forests, boosting, and Viola-Jones face detection. Examples of results from applying these techniques to object classification problems are also provided.
Performance analysis on color image mosaicing techniques on FPGA
Today, the surveillance systems and other monitoring systems are considering the capturing of image sequences in a single frame. The captured images can be combined to get the mosaiced image or combined image sequence. But the captured image may have quality issues like brightness issue, alignment issue (correlation issue), resolution issue, manual image registration issue etc. The existing technique like cross correlation can offer better image mosaicing but faces brightness issue in mosaicing. Thus, this paper introduces two different methods for mosaicing i.e., (a) Sliding Window Module (SWM) based Color Image Mosaicing (CIM) and (b) Discrete Cosine Transform (DCT) based CIM on Field Programmable Gate Array (FPGA). The SWM based CIM adopted for corner detection of two images and perform the automatic image registration while DCT based CIM aligns both the local as well as global alignment of images by using phase correlation approach. Finally, these two methods performances are analyzed by comparing with parameters like PSNR, MSE, device utilization and execution time. From the analysis it is concluded that the DCT based CIM can offers significant results than SWM based CIM.
[PR12] Generative Models as Distributions of Functions
Introduction to Generative Models as Distributions of Functions
video: https://youtu.be/t2oyFXPLUwU
paper: https://arxiv.org/abs/2102.04776
Object detection presentation
The document describes a project that aims to develop a mobile application for real-time object and pose detection. The application will take in a real-time image as input and output bounding boxes identifying the objects in the image along with their class. The methodology involves preprocessing the image, then using the YOLO framework for object classification and localization. The goals are to achieve high accuracy detection that can be used for applications like vehicle counting and human activity recognition.
NIPS2015 reading - Learning visual biases from human imagination
1) The document discusses a paper on improving visual recognition systems by leveraging human visual biases and generating images from random features.
2) It describes estimating visual biases from human psychophysics experiments, then using those biases to reconstruct images from random features. The reconstructed images can then be used to train machine learning models.
3) The document outlines experiments showing that incorporating estimated human visual biases into machine learning models, such as SVMs, can help improve visual recognition performance compared to models trained without biases.
Tracking Chessboard Corners Using Projective Transformation for Augmented Rea...
Augmented reality has been a topic of intense research for several years for many applications. It consists of inserting a virtual object into a real scene. The virtual object must be accurately positioned in a desired place. Some measurements (calibration) are thus required and a set of correspondences between points on the calibration target and the camera images must be found. In this paper, we present a tracking technique based on both detection of Chessboard corners and a least squares method; the objective is to estimate the perspective transformation matrix for the current view of the camera. This technique does not require any information or computation of the camera parameters; it can used in real time without any initialization and the user can change the camera focal without any fear of losing alignment between real and virtual object.
Image analytics - A Primer
An Intro to Image Analytics. A slightly rehashed version of Computer Vision - Old Problems and New Solutions PPT
“An Introduction to Data Augmentation Techniques in ML Frameworks,” a Present...
“An Introduction to Data Augmentation Techniques in ML Frameworks,” a Present...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/09/an-introduction-to-data-augmentation-techniques-in-ml-frameworks-a-presentation-from-amd/
Rajy Rawther, PMTS Software Architect at AMD, presents the “Introduction to Data Augmentation Techniques in ML Frameworks” tutorial at the May 2021 Embedded Vision Summit.
Data augmentation is a set of techniques that expand the diversity of data available for training machine learning models by generating new data from existing data. This talk introduces different types of data augmentation techniques as well as their uses in various training scenarios.
Rawther explores some built-in augmentation methods in popular ML frameworks like PyTorch and TensorFlow. She also discusses some tips and tricks that are commonly used to randomly select parameters to avoid having model overfit to a particular dataset.BOIL: Towards Representation Change for Few-shot Learning
Hyungjun Yoo is defending his master's thesis which discusses meta-learning and representation change. The document introduces meta-learning and its goal of learning to learn from previous tasks to quickly adapt to new tasks. It describes common meta-learning algorithms like MAML and ANIL and proposes the BOIL algorithm which updates only the body in the inner loop. The document argues that representation change is necessary for domain-agnostic adaptation across different domains, as representation reuse may not generalize when the source and target domains are dissimilar. BOIL facilitates representation change through body updates in the inner loop to enable adapting representations for different target domains.
What's hot (20)
[CVPR2020] Simple but effective image enhancement techniques
[CVPR2020] Simple but effective image enhancement techniques
Color based image processing , tracking and automation using matlab
Color based image processing , tracking and automation using matlab
Face Detection System on Ada boost Algorithm Using Haar Classifiers
Face Detection System on Ada boost Algorithm Using Haar Classifiers
Review : Structure Boundary Preserving Segmentation
for Medical Image with Am...
Review : Structure Boundary Preserving Segmentation
for Medical Image with Am...
Modeling perceptual similarity and shift invariance in deep networks
Modeling perceptual similarity and shift invariance in deep networks
Performance analysis on color image mosaicing techniques on FPGA
Performance analysis on color image mosaicing techniques on FPGA
[PR12] Generative Models as Distributions of Functions
[PR12] Generative Models as Distributions of Functions
NIPS2015 reading - Learning visual biases from human imagination
NIPS2015 reading - Learning visual biases from human imagination
Tracking Chessboard Corners Using Projective Transformation for Augmented Rea...
Tracking Chessboard Corners Using Projective Transformation for Augmented Rea...
“An Introduction to Data Augmentation Techniques in ML Frameworks,” a Present...
“An Introduction to Data Augmentation Techniques in ML Frameworks,” a Present...
BOIL: Towards Representation Change for Few-shot Learning
BOIL: Towards Representation Change for Few-shot Learning
Similar to PR100: SeedNet: Automatic Seed Generation with Deep Reinforcement Learning for Robust Interactive Segmentation
Seed net automatic seed generation with deep reinforcement learning for robus...
본 논문에서는 interactive segmentation 문제를 풀기 위하여 deep reinforcement learning을 활용한 seed gereration 기법을 제안한다. Interactive segmentation 문제의 이슈 중 하나는 사용자의 개입을 최소화하는 것이다. 본 논문에서 제안하는 시스템이 사용자를 대신하여 인공적인 seed를 생성하게 된다. 사용자는 initial seed 정보만을 제공하면 된다. 우리는 optimal seed point 정의의 모호함으로 인해 supervised 기법을 사용하여 학습하기 어려운 점을 reinforcement learning 기법을 사용하여 극복하였다. Seed generation 문제에 맞도록 MDP를 정의하여 deep-q-network를 성공적으로 학습하였다. 우리는 MSRA10K 데이터셋에 대하여 학습을 진행하여 기존 segmentation 알고리즘의 부정확한 initial 결과 대비 우수한 성능을 보였다.
final ppt
The document presents a project report on machine learning. It discusses several projects completed including implementing neural networks to compute averages, extracting histogram of joints features, and developing a gesture recognition system using Hidden Markov Models. The gesture recognition system uses a Kinect sensor to capture skeleton data, extracts features, builds a codebook using clustering, trains HMM models for each gesture, and achieves over 85% accuracy on a dataset of 15 gestures. Future work to improve the system is also outlined.
Learning with Relative Attributes
This document is a project report submitted by Shubham Jain and Vikas Jain for their course CS676A. The project aims to learn relative attributes associated with face images using the PubFig dataset. Convolutional neural network features and the RankNet model were used to predict attribute rankings. RankNet achieved better performance than RankSVM and GIST features. Zero-shot learning for unseen classes was explored by building probabilistic class models, but performance was poor. Future work could improve the modeling of unseen classes.
#6 PyData Warsaw: Deep learning for image segmentation
Deep learning techniques ignited a great progress in many computer vision tasks like image classification, object detection, and segmentation. Almost every month a new method is published that achieves state-of-the-art result on some common benchmark dataset. In addition to that, DL is being applied to new problems in CV.
In the talk we’re going to focus on DL application to image segmentation task. We want to show the practical importance of this task for the fashion industry by presenting our case study with results achieved with various attempts and methods.
Recent advances of AI for medical imaging : Engineering perspectives
인공지능과 빅데이터 시대의 영상의학 심포지엄 발표자료
"Recent advances of AI for medical imaging : Engineering perspectives"
Course Title CS591-Advance Artificial Intelligence
Course Title: CS591-Advance Artificial Intelligence StudentName: Namratha Valle, Malemarpuram Chaitanya
sai, Sasidhar Reddy Vajrala, Nagendra Mokara SEMOID#S02023694
StudentEmail: [email protected] Date:04/20/2021
Violations of academic honesty represent a serious breach of discipline and may be considered grounds for disciplinary action, including dismissal
from the University. The University requires that all assignments submitted to faculty members by students be the work of the individual student
submitting the work. An exception would be group projects assigned by the instructor. (Source: SEMO website)
Advanced Artificial Intelligence Assignment
Graduate project level 2
Abstract
Artificial Intelligence (AI) is a crucial technical technology that is commonly used in today's
society. Deep Learning, in particular, has a variety of uses due to its ability to learn robust
representations from images. A Convolutional Neural Network (CNN) is a Deep Learning
algorithm which commands the input image, assigns significance to numerous aspects/objects in
the image, and can distinguish between them. For image classification, CNN is the most popular
Deep Learning architecture. To get better results, we used various automated processing tasks for
fruit and vegetable images. In comparison to other classification deep learning algorithms, the
amount of pre-processing needed by a CNN model is much lower. Furthermore, the learning
capabilities of Deep Learning architectures can be used to improve sound classification in order
to solve efficiency problems. CNN is used in this project, and layers are created to classify the
sound waves into their various categories.
Introduction
We humans enjoy analyzing items, and everything you can think of can be classified into a
classification or class. It is an everyday issue in business; analysis of parts, installations,
gatherings, and products are necessary for the daily routine. This is the reason why people have
devised procedures such as Machine Learning (ML), Neural Networks (NN), and Deep Learning
(DL), among other calculations, to automate the arrangement period. Deep learning will be one
of them that we will explore. Deep learning is an artificial intelligence (AI) function that
simulates how the human brain processes data and creates patterns to make decisions. The
classification of photographs of fruits and vegetables with the naked eye is very difficult. As a
result, we're using pyTorch to process image datasets with Deep Learning. We're developing a
CNN model for image detection and categorization using these datasets. A custom CNN is
introduced and then compared to a ResNet CNN for the purposes of this study. The oth ...
Avihu Efrat's Viola and Jones face detection slides
The document summarizes the Viola-Jones object detection framework. It uses a cascade of classifiers with increasingly more complex features trained with AdaBoost to rapidly detect objects. Integral images allow for very fast feature evaluations. The framework was applied to face detection, achieving very fast average detection speeds of 270 microseconds per sub-window while maintaining low false positive rates.
ML Paper Tutorial - Video Face Manipulation Detection Through Ensemble of CNN...
This is a slide deck for paper review. This paper is about using a machine learning algorithm to detect deepfake images.
AI and Deep Learning
This talk was presented in Startup Master Class 2017 - http://aaiitkblr.org/smc/ 2017 @ Christ College Bangalore. Hosted by IIT Kanpur Alumni Association and co-presented by IIT KGP Alumni Association, IITACB, PanIIT, IIMA and IIMB alumni.
My co-presenter was Biswa Gourav Singh. And contributor was Navin Manaswi.
http://dataconomy.com/2017/04/history-neural-networks/ - timeline for neural networks
Performance evaluation of GANs in a semisupervised OCR use case
This document discusses using generative adversarial networks (GANs) for a semi-supervised optical character recognition (OCR) use case involving vehicle identification numbers (VINs). It describes the text spotting pipeline, challenges with limited training data, data augmentation techniques, and implementing a GAN for character detection. Evaluation shows the semi-supervised GAN approach outperforms other methods, achieving over 99% accuracy on VIN detection and recognition from images using only 85 labeled examples. Key learnings include that custom solutions can outperform off-the-shelf tools for specific tasks, and GANs are well-suited for problems with limited labeled data when combined with data augmentation.
Performance evaluation of GANs in a semisupervised OCR use case
Online vehicle marketplaces are embracing artificial intelligence to ease the process of selling a vehicle on their platform. The tedious work of copying information from the vehicle registration document into some web form can be automated with the help of smart text-spotting systems, in which the seller takes a picture of the document, and the necessary information is extracted automatically.
Florian Wilhelm details the components of a text-spotting system, including the subtasks of object detection and optical character recognition (OCR). Florian elaborates on the challenges of OCR in documents with various distortions and artifacts, which rule out off-the-shelf products for this task. After offering an overview of semisupervised learning based on generative adversarial networks (GANs), Florian evaluates the performance gains of this method compared to supervised learning. More specifically, for a varying amount of labeled data, he compares the accuracy of a convolution neural network (CNN) to a GANthat uses additional unlabeled data during the training phase, showing that GANs significantly outperform classical CNNs in use cases with a lack of labeled data.
What you'll learn:
Understand how semisupervised learning with GANs works
Explore beneficial semisupervised methods based on GANs for use cases with a limited amount of labeled data
Gain insight into an interesting OCR use case of an online vehicle marketplace
Event: O'Reilly Artificial Intelligence Conference, London, 11.10.2018
Speaker: Dr. Florian Wilhelm
Mehr Tech-Vorträge: www.inovex.de/vortraege
Mehr Tech-Artikel: www.inovex.de/blog
Rapid object detection using boosted cascade of simple features
1. The document presents the seminal work of Viola and Jones on rapid object detection using boosted cascades of simple features.
2. It introduces integral images for fast feature evaluation and uses AdaBoost for feature selection and classifier training in a cascade structure.
3. The cascade approach combines classifiers such that earlier ones rapidly reject negatives while later ones focus on positives, achieving real-time detection rates.
Unsupervised Computer Vision: The Current State of the Art
This presentation was originally given at a styling research presentation at Stitch Fix, where I talk about some of the recent progress in the field of unsupervised deep learning methods for image analysis. It includes descriptions of Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), their hybrid (VAE/GAN), Generative Moment Matching Networks (GMMN), and Adversarial Autoencoders.
Atari Game State Representation using Convolutional Neural Networks
I recently gave a talk to some MSc Machine Learning students at De Montfort University about the project I did for my MSc. The work included looking at feature extraction from game screens using the Arcade Learning Environment and Convolutional Neural Networks (CNN).
The work was planned to investigate if the costly nature Q-Learning could be offset by the use of a trained system using 'expert' data. The system uses the same technology as used by Deepmind in their 2013 paper.
N046047780
This document summarizes a research paper that proposes a new technique called Morphology based technique for Extraction and Detection of blinking Region from gif Images. It begins by introducing the goal of detecting blinking parts in gif images and issues with existing techniques. It then describes the proposed methodology which uses edge detection, morphological operations like closing, and precision/recall metrics to evaluate the technique. The methodology is tested on sample gif images and results show high precision and recall rates, indicating the model is effective at extracting blinking regions.
B4UConference_machine learning_deeplearning
Agenda
Understanding Machine Learning
Machine Learning Algorithms & Application
Demo Machine Learning
Why Deep Learning?
Deep Learning Algorithms & Application
Demo Deep Learning
K-Means Clustering in Moving Objects Extraction with Selective Background
K-Means Clustering in Moving Objects Extraction with Selective BackgroundIJCSIS Research Publications
We presents a technique for moving objects extraction. There are several different approaches for moving object extraction, clustering is one of object extraction method with a stronger teorical foundation used in many applications. And need high performance in many extraction process of moving object. We compare K-Means and Self-Organizing Map method for extraction moving objects, for performance measurement of moving object extraction by applying MSE and PSNR. According to experimental result that the MSE value of K-Means is smaller than Self-Organizing Map. It is also that PSNR of K-Means is higher than Self-Organizing Map algorithm. The result proves that K-Means is a promising method to cluster pixels in moving objects extraction.brief Introduction to Different Kinds of GANs
Generative adversarial networks (GANs) are introduced, including the basic GAN framework containing a generator and discriminator. Various types of GANs are then discussed, such as DCGANs, semi-supervised GANs, and character GANs. The document concludes with a summary of resources on GANs and applications such as image-to-image translation and conditional waveform synthesis.
Eren_Golge_MS_Thesis_2014
This Presentation is used in the dissertation of the Eren Golge's Master of Science. It proposes 2 new procedure to learn visual concept models from noisy image sources without any human annotation.
Image classification with Deep Neural Networks
This document discusses image classification using deep neural networks. It provides background on image classification and convolutional neural networks. The document outlines techniques like activation functions, pooling, dropout and data augmentation to prevent overfitting. It summarizes a paper on ImageNet classification using CNNs with multiple convolutional and fully connected layers. The paper achieved state-of-the-art results on ImageNet in 2010 and 2012 by training CNNs on a large dataset using multiple GPUs.
Similar to PR100: SeedNet: Automatic Seed Generation with Deep Reinforcement Learning for Robust Interactive Segmentation (20)
Seed net automatic seed generation with deep reinforcement learning for robus...
Seed net automatic seed generation with deep reinforcement learning for robus...
#6 PyData Warsaw: Deep learning for image segmentation
#6 PyData Warsaw: Deep learning for image segmentation
Recent advances of AI for medical imaging : Engineering perspectives
Recent advances of AI for medical imaging : Engineering perspectives
Course Title CS591-Advance Artificial Intelligence
Course Title CS591-Advance Artificial Intelligence
Avihu Efrat's Viola and Jones face detection slides
Avihu Efrat's Viola and Jones face detection slides
ML Paper Tutorial - Video Face Manipulation Detection Through Ensemble of CNN...
ML Paper Tutorial - Video Face Manipulation Detection Through Ensemble of CNN...
Performance evaluation of GANs in a semisupervised OCR use case
Performance evaluation of GANs in a semisupervised OCR use case
Performance evaluation of GANs in a semisupervised OCR use case
Performance evaluation of GANs in a semisupervised OCR use case
Rapid object detection using boosted cascade of simple features
Rapid object detection using boosted cascade of simple features
Unsupervised Computer Vision: The Current State of the Art
Unsupervised Computer Vision: The Current State of the Art
Atari Game State Representation using Convolutional Neural Networks
Atari Game State Representation using Convolutional Neural Networks
K-Means Clustering in Moving Objects Extraction with Selective Background
K-Means Clustering in Moving Objects Extraction with Selective Background
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Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Northern Engraving | Nameplate Manufacturing Process - 2024
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
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Fueling AI with Great Data with Airbyte Webinar
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Energy Efficient Video Encoding for Cloud and Edge Computing Instances
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A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
The Microsoft 365 Migration Tutorial For Beginner.pptx
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
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Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Dmitrii Kamaev, PhD
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Choosing The Best AWS Service For Your Website + API.pptx
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
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June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...Pitangent Analytics & Technology Solutions Pvt. Ltd
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.Mutation Testing for Task-Oriented Chatbots
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
"Choosing proper type of scaling", Olena Syrota
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Essentials of Automations: Exploring Attributes & Automation Parameters
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
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PR100: SeedNet: Automatic Seed Generation with Deep Reinforcement Learning for Robust Interactive Segmentation
- 1. PR-100: SeedNet: Automatic Seed Generation with Deep Reinforcement Learning for Robust Interactive Segmentation CVPR2018 Gwangmo Song, Heesoo Myeong, Kyoung Mu Lee 인공지능연구원 이광희
- 2. 2 논문 선정의 이유.. Chen, Tao, et al. "PhotoSketch: Internet image montage." SIGGRAPH Asia (2009).
- 3. 3 논문 선정의 이유.. 스케치 배경 오브젝트 사진 선택 이미지 조합 사용될 이미지 생성 팔레트 최종 결과물 텍스트소나무 + 전체 스타일 변환 (팔레트) 브러시 부분 수정 및 조정 (브러시) 이미지 생성 모델 스타일 변환 모델 검색
- 5. 5 Related Works : Interactive Segmentation Deep extreme cut: From extreme points to object segmentation. CVPR2018 Grabcut: Interactive foreground extraction using iterated graph cuts. Siggraph2003 Methods: Grabcut Random walk Geodesic Deep extremecut . . . Seed types: Rectangle Scribble Contour Extreme point . .
- 6. 6 Classification, image captioning, video tracking, face hallucination, … Related Works : RL in Computer Vision Active Object Localization with Deep Reinforcement Learning. ICCV2015 Distort-and-Recover: Color enhancement using deep reinforcement learning. CVPR2018
- 7. 7 An automatic seed generation technique with deep RL to solve the interactive segmentation problem Robust and consistent object extraction with less human effort User first select two points- foreground & background A sequence of artificial user input is automatically generated Markov Decision Process(MDP) / Deep Q-Network(DQN) Motivation
- 8. 8 Introduction of a MDP formulation for the interactive segmentation task The novel reward function design: Intersection Over Union(IOU) score Why deep RL? • Cannot define globally optimal seed at some stage of interactive segmentation Contributions
- 9. 9 Automatic Seed Generation System Markov Decision Process(MDP) - State: input image + segmented mask by new seeds - Action: 800 actions, label(fg/bg), position of the seed in the 2D grid(20x20) - Reward: Segmentation method: Random Walk(RW) segmentation Binary Mask - Compute reward signal - An observation of the next iteration Termination: 10 seed points DQN architecture SF: Strong Foreground SB: Strong Background WF: Weak Foreground WB: Weak Background
- 10. 10 Experiments MSRA10K saliency dataset Training: 9000 images, Test: 1000 images, Total: 10000 images Image size: about 400x300 pixels Training/testing Input size: 84x84 Segmentation • Training: 84x84(for accelerate), seed point size(3 pixels) • Testing: original size, seed point size(13 pixels) Termination: 10 times (average number of seeds until saturation: 5.39)
- 12. 12 Comparison with supervised methods FCN/iFCN Fully-connected layer to convolutional layer 80x80 input image Interactive Segmentation Results
- 16. PR12 1기 멤버분들 모두 1년동안 수고 많으셨습니다.