The document discusses recent work on high-resolution representation learning for computer vision tasks like image classification, semantic segmentation, object detection, and pose estimation. It introduces HRNet, a new convolutional neural network architecture that maintains high-resolution representations through the entire network using repeated multi-scale fusions. HRNet achieves state-of-the-art results on several benchmarks, demonstrating that high-resolution representations are important for dense prediction and pixel-level tasks. The document also discusses related approaches and provides details of HRNet's implementation and performance.
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
Enhanced Deep Residual Networks for Single Image Super-ResolutionNAVER Engineering
발표자: 김희원 (서울대학교 박사과정)
발표일: 2017.9.
(현)서울대학교 전기정보공학 석박통합과정 재학
Best Paper Award of NTIRE 2017 Workshop: Challenge Track
개요:
Single Image Super-Resolution은 저해상도 이미지를 고해상도의 원본 이미지로 복원시키는 연구 분야입니다. 실생활에서 접할 수 있는 흔한 예로는 SNS 사진 중 작은 부분을 크게 확대해도 선명하게 하는 것이나, thumb nail로 원본 이미지만큼의 해상도를 만들어 내는 것입니다.
이번 발표에서는 딥러닝 전과 후의 연구방향에 대해서 알아본 후, CVPR 2017의 2nd NTIRE Workshop Challenge에서 우승한 저희 팀의 연구를 신경망 구조에 대한 분석을 중심으로 살펴보려고 합니다.
발표자: 전석준(KAIST 박사과정)
발표일: 2018.8.
Super-resolution은 저해상도 이미지를 고해상도 이미지로 변환시키는 기술로 오랜기간 연구되어 온 주제입니다. 최근 딥러닝 기술이 적용됨에 따라 super-resolution 성능이 비약적으로 향상되었습니다. 저희는 스테레오 이미지를 이용하여 더 높은 해상도의 이미지를 얻는 기술을 개발하였습니다. 이에 관련 내용을 발표하고자 합니다.
1. Multi-Frame Super-Resolution
2. Learning-Based Super-Resolution
3. Stereo Imaging
4. Deep-Learning Based Stereo Super-Resolution
A Novel and Robust Wavelet based Super Resolution Reconstruction of Low Resol...CSCJournals
High Resolution images can be reconstructed from several blurred, noisy and aliased low resolution images using a computational process know as super resolution reconstruction. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. In this paper we concentrate on a special case of super resolution problem where the wrap is composed of pure translation and rotation, the blur is space invariant and the noise is additive white Gaussian noise. Super resolution reconstruction consists of registration, restoration and interpolation phases. Once the Low resolution image are registered with respect to a reference frame then wavelet based restoration is performed to remove the blur and noise from the images, finally the images are interpolated using adaptive interpolation. We are proposing an efficient wavelet based denoising with adaptive interpolation for super resolution reconstruction. Under this frame work, the low resolution images are decomposed into many levels to obtain different frequency bands. Then our proposed novel soft thresholding technique is used to remove the noisy coefficients, by fixing optimum threshold value. In order to obtain an image of higher resolution we have proposed an adaptive interpolation technique. Our proposed wavelet based denoising with adaptive interpolation for super resolution reconstruction preserves the edges as well as smoothens the image without introducing artifacts. Experimental results show that the proposed approach has succeeded in obtaining a high-resolution image with a high PSNR, ISNR ratio and a good visual quality.
Deep learning for image super resolutionPrudhvi Raj
Using Deep Convolutional Networks, the machine can learn end-to-end mapping between the low/high-resolution images. Unlike traditional methods, this method jointly optimizes all the layers of the image. A light-weight CNN structure is used, which is simple to implement and provides formidable trade-off from the existential methods.
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
Enhanced Deep Residual Networks for Single Image Super-ResolutionNAVER Engineering
발표자: 김희원 (서울대학교 박사과정)
발표일: 2017.9.
(현)서울대학교 전기정보공학 석박통합과정 재학
Best Paper Award of NTIRE 2017 Workshop: Challenge Track
개요:
Single Image Super-Resolution은 저해상도 이미지를 고해상도의 원본 이미지로 복원시키는 연구 분야입니다. 실생활에서 접할 수 있는 흔한 예로는 SNS 사진 중 작은 부분을 크게 확대해도 선명하게 하는 것이나, thumb nail로 원본 이미지만큼의 해상도를 만들어 내는 것입니다.
이번 발표에서는 딥러닝 전과 후의 연구방향에 대해서 알아본 후, CVPR 2017의 2nd NTIRE Workshop Challenge에서 우승한 저희 팀의 연구를 신경망 구조에 대한 분석을 중심으로 살펴보려고 합니다.
발표자: 전석준(KAIST 박사과정)
발표일: 2018.8.
Super-resolution은 저해상도 이미지를 고해상도 이미지로 변환시키는 기술로 오랜기간 연구되어 온 주제입니다. 최근 딥러닝 기술이 적용됨에 따라 super-resolution 성능이 비약적으로 향상되었습니다. 저희는 스테레오 이미지를 이용하여 더 높은 해상도의 이미지를 얻는 기술을 개발하였습니다. 이에 관련 내용을 발표하고자 합니다.
1. Multi-Frame Super-Resolution
2. Learning-Based Super-Resolution
3. Stereo Imaging
4. Deep-Learning Based Stereo Super-Resolution
A Novel and Robust Wavelet based Super Resolution Reconstruction of Low Resol...CSCJournals
High Resolution images can be reconstructed from several blurred, noisy and aliased low resolution images using a computational process know as super resolution reconstruction. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. In this paper we concentrate on a special case of super resolution problem where the wrap is composed of pure translation and rotation, the blur is space invariant and the noise is additive white Gaussian noise. Super resolution reconstruction consists of registration, restoration and interpolation phases. Once the Low resolution image are registered with respect to a reference frame then wavelet based restoration is performed to remove the blur and noise from the images, finally the images are interpolated using adaptive interpolation. We are proposing an efficient wavelet based denoising with adaptive interpolation for super resolution reconstruction. Under this frame work, the low resolution images are decomposed into many levels to obtain different frequency bands. Then our proposed novel soft thresholding technique is used to remove the noisy coefficients, by fixing optimum threshold value. In order to obtain an image of higher resolution we have proposed an adaptive interpolation technique. Our proposed wavelet based denoising with adaptive interpolation for super resolution reconstruction preserves the edges as well as smoothens the image without introducing artifacts. Experimental results show that the proposed approach has succeeded in obtaining a high-resolution image with a high PSNR, ISNR ratio and a good visual quality.
Deep learning for image super resolutionPrudhvi Raj
Using Deep Convolutional Networks, the machine can learn end-to-end mapping between the low/high-resolution images. Unlike traditional methods, this method jointly optimizes all the layers of the image. A light-weight CNN structure is used, which is simple to implement and provides formidable trade-off from the existential methods.
Super resolution in deep learning era - Jaejun YooJaeJun Yoo
Abstract (Eng/Kor):
Image restoration (IR) is one of the fundamental problems, which includes denoising, deblurring, super-resolution, etc. Among those, in today's talk, I will more focus on the super-resolution task. There are two main streams in the super-resolution studies; a traditional model-based optimization and a discriminative learning method. I will present the pros and cons of both methods and their recent developments in the research field. Finally, I will provide a mathematical view that explains both methods in a single holistic framework, while achieving the best of both worlds. The last slide summarizes the remaining problems that are yet to be solved in the field.
영상 복원(Image restoration, IR)은 low-level vision에서 매우 중요하게 다루는 근본적인 문제 중 하나로서 denoising, deblurring, super-resolution 등의 다양한 영상 처리 문제를 포괄합니다. 오늘 발표에서는 영상 복원 분야 중에서도 super-resolution 문제에 대해 집중적으로 다루겠습니다. 전통적인 model-based optimization 방식과 deep learning을 적용하여 문제를 푸는 방식에 대해, 각각의 장단점과 최신 연구 발전 흐름을 소개하겠습니다. 마지막으로는 이 둘을 하나로 잇는 통일된 관점을 제시하고 관련 연구들 살펴본 후, super-resolution 분야에서 아직 남아있는 문제점들을 정리하겠습니다.
발표자: 배재성(KAIST 석사과정)
발표일: 2018.10.
최근 딥러닝을 이용한 방법은 다양한 음성 인식 과제에서 괄목할 만한 성과를 내고 있습니다. 특히 Convolutional Neural Network (CNN)을 이용한 방식은 지역적인 특징 (local feature)들을 효과적으로 잡아낼 수 있기 때문에 비교적 짧은 시간 의존도를 가지는 음성 키워드 인식이나 음소 단위 인식과 같은 과제들에서 활발히 사용되고 있습니다. 그러나 CNN은 낮은 레벨의 특징들 간의 공간적 관계성을 고려하지 않는다는 한계점이 있습니다. 이를 극복하기 위해 캡슐 네트워크 구조를 도입하여 음성 스펙트로그램에서 추출된 특징들의 공간적 관계성을 고려하고자 하였습니다. 구글 음성 단어 데이터셋에서 CNN과 그 성능을 비교해 보았으며, 깨끗한 환경과 잡음 환경 모두에서 주목할만한 성능 향상을 이끌어 냈습니다.
Scaling up Deep Learning Based Super Resolution AlgorithmsXiaoyong Zhu
Superresolution is a process for obtaining one or more high-resolution images from one or more low-resolution observations. It has been used for many applications, including satellite and aerial imaging, medical image processing, ultrasound imaging, line fitting, automated mosaicking, infrared imaging, facial image improvement, text image improvement, compressed image and video enhancement, and fingerprint image enhancement. While research on superresolution began in the 1970s, recently, with the power of deep learning, many notable new methods have been created, including SRCNN, SRResNet, and lately, SRGANs, which use generative adversarial networks. However, since these approaches require a lot of images to train the deep learning network, they are supercompute intensive. Fortunately, with the power of the cloud, you can easily scale up the compute resources as needed, making the algorithm converge faster.
AN ENHANCEMENT FOR THE CONSISTENT DEPTH ESTIMATION OF MONOCULAR VIDEOS USING ...mlaij
Depth estimation has made great progress in the last few years due to its applications in robotics science
and computer vision. Various methods have been implemented and enhanced to estimate the depth without
flickers and missing holes. Despite this progress, it is still one of the main challenges for researchers,
especially for the video applications which have more complexity of the neural network which af ects the
run time. Moreover to use such input like monocular video for depth estimation is considered an attractive
idea, particularly for hand-held devices such as mobile phones, they are very popular for capturing
pictures and videos, in addition to having a limited amount of RAM. Here in this work, we focus on
enhancing the existing consistent depth estimation for monocular videos approach to be with less usage of
RAM and with using less number of parameters without having a significant reduction in the quality of the
depth estimation.
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.
Super resolution in deep learning era - Jaejun YooJaeJun Yoo
Abstract (Eng/Kor):
Image restoration (IR) is one of the fundamental problems, which includes denoising, deblurring, super-resolution, etc. Among those, in today's talk, I will more focus on the super-resolution task. There are two main streams in the super-resolution studies; a traditional model-based optimization and a discriminative learning method. I will present the pros and cons of both methods and their recent developments in the research field. Finally, I will provide a mathematical view that explains both methods in a single holistic framework, while achieving the best of both worlds. The last slide summarizes the remaining problems that are yet to be solved in the field.
영상 복원(Image restoration, IR)은 low-level vision에서 매우 중요하게 다루는 근본적인 문제 중 하나로서 denoising, deblurring, super-resolution 등의 다양한 영상 처리 문제를 포괄합니다. 오늘 발표에서는 영상 복원 분야 중에서도 super-resolution 문제에 대해 집중적으로 다루겠습니다. 전통적인 model-based optimization 방식과 deep learning을 적용하여 문제를 푸는 방식에 대해, 각각의 장단점과 최신 연구 발전 흐름을 소개하겠습니다. 마지막으로는 이 둘을 하나로 잇는 통일된 관점을 제시하고 관련 연구들 살펴본 후, super-resolution 분야에서 아직 남아있는 문제점들을 정리하겠습니다.
발표자: 배재성(KAIST 석사과정)
발표일: 2018.10.
최근 딥러닝을 이용한 방법은 다양한 음성 인식 과제에서 괄목할 만한 성과를 내고 있습니다. 특히 Convolutional Neural Network (CNN)을 이용한 방식은 지역적인 특징 (local feature)들을 효과적으로 잡아낼 수 있기 때문에 비교적 짧은 시간 의존도를 가지는 음성 키워드 인식이나 음소 단위 인식과 같은 과제들에서 활발히 사용되고 있습니다. 그러나 CNN은 낮은 레벨의 특징들 간의 공간적 관계성을 고려하지 않는다는 한계점이 있습니다. 이를 극복하기 위해 캡슐 네트워크 구조를 도입하여 음성 스펙트로그램에서 추출된 특징들의 공간적 관계성을 고려하고자 하였습니다. 구글 음성 단어 데이터셋에서 CNN과 그 성능을 비교해 보았으며, 깨끗한 환경과 잡음 환경 모두에서 주목할만한 성능 향상을 이끌어 냈습니다.
Scaling up Deep Learning Based Super Resolution AlgorithmsXiaoyong Zhu
Superresolution is a process for obtaining one or more high-resolution images from one or more low-resolution observations. It has been used for many applications, including satellite and aerial imaging, medical image processing, ultrasound imaging, line fitting, automated mosaicking, infrared imaging, facial image improvement, text image improvement, compressed image and video enhancement, and fingerprint image enhancement. While research on superresolution began in the 1970s, recently, with the power of deep learning, many notable new methods have been created, including SRCNN, SRResNet, and lately, SRGANs, which use generative adversarial networks. However, since these approaches require a lot of images to train the deep learning network, they are supercompute intensive. Fortunately, with the power of the cloud, you can easily scale up the compute resources as needed, making the algorithm converge faster.
AN ENHANCEMENT FOR THE CONSISTENT DEPTH ESTIMATION OF MONOCULAR VIDEOS USING ...mlaij
Depth estimation has made great progress in the last few years due to its applications in robotics science
and computer vision. Various methods have been implemented and enhanced to estimate the depth without
flickers and missing holes. Despite this progress, it is still one of the main challenges for researchers,
especially for the video applications which have more complexity of the neural network which af ects the
run time. Moreover to use such input like monocular video for depth estimation is considered an attractive
idea, particularly for hand-held devices such as mobile phones, they are very popular for capturing
pictures and videos, in addition to having a limited amount of RAM. Here in this work, we focus on
enhancing the existing consistent depth estimation for monocular videos approach to be with less usage of
RAM and with using less number of parameters without having a significant reduction in the quality of the
depth estimation.
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.
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.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
PR-183: MixNet: Mixed Depthwise Convolutional KernelsJinwon Lee
TensorFlow-KR 논문읽기모임 PR12(12PR) 183번째 논문 review입니다.
이번에 살펴볼 논문은 Google Brain에서 발표한 MixNet입니다. Efficiency를 추구하는 CNN에서 depthwise convolution이 많이 사용되는데, 이 때 depthwise convolution filter의 size를 다양하게 해서 성능도 높이고 efficiency도 높이는 방법을 제안한 논문입니다. 자세한 내용은 영상을 참고해주세요
논문링크 : https://arxiv.org/abs/1907.09595
발표영상 : https://youtu.be/252YxqpHzsg
Artificial Intelligence (AI), specifically deep learning, is revolutionizing industries, products, and core capabilities by delivering dramatically enhanced experiences. However, the deep neural networks of today use too much memory, compute, and energy. Plus, to make AI truly ubiquitous, networks need to run on the end device within a tight power and thermal budget. One approach to help address these issues is quantization, which attempts to reduce the number of bits used for weight parameters and activation calculations without sacrificing model accuracy. This presentation covers: why quantization is important, existing quantization challenges, Qualcomm AI Research's existing quantization research, and how developers and researchers can take advantage of quantization on Qualcomm Snapdragon.
InternImage: Exploring Large-Scale Vision Foundation Models with Deformable C...taeseon ryu
요즘 대형 비전 트랜스포머(ViT)의 발전에 비해, 합성곱 신경망(CNN)을 기반으로 한 대형 모델은 아직 초기 단계에 머물러 있습니다. 본 연구는 InternImage라는 새로운 대규모 CNN 기반 모델을 제안합니다. 이 모델은 ViT와 같이 매개변수와 학습 데이터를 늘리는 이점을 얻을 수 있습니다. 최근에는 대형 밀집 커널에 초점을 맞춘 CNN과는 달리, InternImage는 변형 가능한 컨볼루션을 핵심 연산자로 사용합니다. 이를 통해 모델은 감지 및 세분화와 같은 하향 작업에 필요한 큰 유효 수용영역을 갖게 되며, 입력 및 작업 정보에 의존하는 적응형 공간 집계도 가능합니다. 이로 인해, InternImage는 기존 CNN의 엄격한 귀납적 편향을 줄이고, ViT와 같은 대규모 매개변수와 대규모 데이터로 더 강력하고 견고한 패턴을 학습할 수 있게 됩니다. 논문에서 제시한 모델의 효과성은 ImageNet, COCO 및 ADE20K와 같은 어려운 벤치마크에서 입증되었습니다. InternImage-H는 COCO test-dev에서 65.4 mAP, ADE20K에서 62.9 mIoU를 달성하여 현재 최고의 CNN 및 ViT를 능가하는 새로운 기록을 세웠습니다
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
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 4: retinex model based low light enhancement
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 2: text centric image style transfer
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 1: prior embedding deep rain removal
Instagram has become one of the most popular social media platforms, allowing people to share photos, videos, and stories with their followers. Sometimes, though, you might want to view someone's story without them knowing.
Gen Z and the marketplaces - let's translate their needsLaura Szabó
The product workshop focused on exploring the requirements of Generation Z in relation to marketplace dynamics. We delved into their specific needs, examined the specifics in their shopping preferences, and analyzed their preferred methods for accessing information and making purchases within a marketplace. Through the study of real-life cases , we tried to gain valuable insights into enhancing the marketplace experience for Generation Z.
The workshop was held on the DMA Conference in Vienna June 2024.
Meet up Milano 14 _ Axpo Italia_ Migration from Mule3 (On-prem) to.pdfFlorence Consulting
Quattordicesimo Meetup di Milano, tenutosi a Milano il 23 Maggio 2024 dalle ore 17:00 alle ore 18:30 in presenza e da remoto.
Abbiamo parlato di come Axpo Italia S.p.A. ha ridotto il technical debt migrando le proprie APIs da Mule 3.9 a Mule 4.4 passando anche da on-premises a CloudHub 1.0.
Italy Agriculture Equipment Market Outlook to 2027harveenkaur52
Agriculture and Animal Care
Ken Research has an expertise in Agriculture and Animal Care sector and offer vast collection of information related to all major aspects such as Agriculture equipment, Crop Protection, Seed, Agriculture Chemical, Fertilizers, Protected Cultivators, Palm Oil, Hybrid Seed, Animal Feed additives and many more.
Our continuous study and findings in agriculture sector provide better insights to companies dealing with related product and services, government and agriculture associations, researchers and students to well understand the present and expected scenario.
Our Animal care category provides solutions on Animal Healthcare and related products and services, including, animal feed additives, vaccination
Understanding User Behavior with Google Analytics.pdfSEO Article Boost
Unlocking the full potential of Google Analytics is crucial for understanding and optimizing your website’s performance. This guide dives deep into the essential aspects of Google Analytics, from analyzing traffic sources to understanding user demographics and tracking user engagement.
Traffic Sources Analysis:
Discover where your website traffic originates. By examining the Acquisition section, you can identify whether visitors come from organic search, paid campaigns, direct visits, social media, or referral links. This knowledge helps in refining marketing strategies and optimizing resource allocation.
User Demographics Insights:
Gain a comprehensive view of your audience by exploring demographic data in the Audience section. Understand age, gender, and interests to tailor your marketing strategies effectively. Leverage this information to create personalized content and improve user engagement and conversion rates.
Tracking User Engagement:
Learn how to measure user interaction with your site through key metrics like bounce rate, average session duration, and pages per session. Enhance user experience by analyzing engagement metrics and implementing strategies to keep visitors engaged.
Conversion Rate Optimization:
Understand the importance of conversion rates and how to track them using Google Analytics. Set up Goals, analyze conversion funnels, segment your audience, and employ A/B testing to optimize your website for higher conversions. Utilize ecommerce tracking and multi-channel funnels for a detailed view of your sales performance and marketing channel contributions.
Custom Reports and Dashboards:
Create custom reports and dashboards to visualize and interpret data relevant to your business goals. Use advanced filters, segments, and visualization options to gain deeper insights. Incorporate custom dimensions and metrics for tailored data analysis. Integrate external data sources to enrich your analytics and make well-informed decisions.
This guide is designed to help you harness the power of Google Analytics for making data-driven decisions that enhance website performance and achieve your digital marketing objectives. Whether you are looking to improve SEO, refine your social media strategy, or boost conversion rates, understanding and utilizing Google Analytics is essential for your success.
32. [1] Ting Zhang, Guo-Jun Qi, Bin Xiao, Jingdong Wang: Interleaved Group Convolutions. ICCV 2017: 4383-4392
[2] Guotian Xie, Jingdong Wang, Ting Zhang, Jianhuang Lai, Richang Hong, and Guo-JunQi. IGCV2: Interleaved Structured Sparse Convolutional Neural Networks. CVPR 2018.
[3] Ke Sun, Mingjie Li, Dong Liu, and Jingdong Wang. IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks. BMVC 2018. 33
33. IGCV1: Interleaved group convolutions for large models
Ting Zhang, Guo-Jun Qi, Bin Xiao, Jingdong Wang: Interleaved Group Convolutions, ICCV 2017.
34
44. • Primary group convolution
• Secondary group convolution
Regular convolutions are interleaved group convolutions
Primary
Secondary
W11 W12 W21 W22
+ +
W
45
57. Design:
Balance condition minimum total #parameters, three conditions hold:
1) #parameters for (𝐿 − 1) group convolutions are the same
2) #branches for (𝐿 − 1) group convolutions are the same
3) #channels in each branch are the same
How to design 𝐿 group convolutions
#channels in each branch: 𝐾2 = ⋯ = 𝐾𝐿 = 𝐶
1
𝐿−1 = 𝐾
width
IGC block: 1 channel-wise 3 × 3 convolution + (𝐿 − 1) group 1 × 1 convolutions
61
59. #parameters When balance condition holds
3 × 3 1 × 1
How many group convolutions?
𝐿 = log 𝐶 + 1 so that 𝑄 is minimum
Benefit: Further redundancy reduction by composing more (𝐿 > 2) sparse matrices
𝑄 𝐿 = 3 < 𝑄 𝐿 = 2 if 𝐶 > 4
64
69. IGCV3: Interleaved Low-Rank Group Convolutions
Ke Sun, Mingjie Li, Dong Liu, and Jingdong Wang. IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural
Networks. BMVC 2018.
76
72. Ke Sun, Mingjie Li, Dong Liu, and Jingdong Wang. IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural
Networks. BMVC 2018.
small
IGCV3
79
80. Session 2: High-Resolution Representation Learning
Ke Sun, Bin Xiao, Dong Liu, Jingdong Wang: Deep High-Resolution Representation Learning for Human Pose Estimation. CVPR 2019
Ke Sun, Yang Zhao, Borui Jiang, Tianheng Cheng, Bin Xiao, Dong Liu, Yadong Mu, Xinggang Wang, Wenyu Liu, Jingdong Wang: High-Resolution
Representation Learning for labeling pixels and regions (Journal extension).
81. 92
Wider - more channels
Deeper - more layers
Finer -
higher resolution
→ finer
New dimension: go finer towards high-resolution representation learning
deeper → wider
83. 94
Low resolution
is enough
image recog. pixel-level recog.region-level recog.
The high-resolution representation is needed
global position-sensitive
92. 107
series
• Recover from low-resolution representations
• Repeat fusions across resolutions to strengthen high- & low-resolution
representations
parallel
Maintain through the whole process
HRNet can learn high-resolution strong representations