Survey of recent progress in semantic image segmentation with CNNs

Geng, Qichuan; Zhou, Zhong; Cao, Xiaochun

doi:10.1007/s11432-017-9189-6

Cited by 66 publications

(27 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…CNNs are by far the most successfully implemented NN algorithms, which has many real-world applications, particularly in image classification such as object detection, [54] facial recognition, [55] and image segmentation. [56] In the past decade, CNN has seen several major breakthroughs in image processing applications and produced several specialized high-performance frameworks such as AlexNet, [33] YOLO9000, U-net, [57] GoogLeNet, [58] VGG, [59] among others. A standard CNN model comprised three building blocks, which are the data input, feature learning, and training and classification ( Figure 7).…”

Section: Convolution Neural Networkmentioning

confidence: 99%

Machine Learning‐Enabled Smart Sensor Systems

Ren

et al. 2020

Advanced Intelligent Systems

114

View full text Add to dashboard Cite

Recent advancements and major breakthroughs in machine learning (ML) technologies in the past decade have made it possible to collect, analyze, and interpret an unprecedented amount of sensory information. A new era for “smart” sensor systems is emerging that changes the way that conventional sensor systems are used to understand the world. Smart sensor systems have taken advantage of classic and emerging ML algorithms and modern computer hardware to create sophisticated “smart” models that are tailored specifically for sensing applications and fusing diverse sensing modalities to gain a more holistic appreciation of the system being monitored. Herein, a review of the recent sensing applications, which harness ML enabled smart sensor systems, is presented. First well‐known ML algorithms implemented in smart sensor systems for practical sensing applications are discussed. Subsequent sections summarize the practical sensing applications under two major categories: physical and chemical sensing and visual imaging sensing describing how the sensor technologies are coupled with ML “smart” models and how these systems achieve practical benefits. Finally, an outlook on the current trajectory and challenges that will be faced by future smart sensing systems and the opportunities that may be unlocked is provided.

show abstract

Section: Convolution Neural Networkmentioning

confidence: 99%

Machine Learning‐Enabled Smart Sensor Systems

Ren

et al. 2020

Advanced Intelligent Systems

114

View full text Add to dashboard Cite

show abstract

“…Earlier studies [219] have shown the potential of deep learning based approaches. There have been more recent studies [68] which cover a number of methods and compare them on the basis of their reported performance. The work of Garcia et al [66] lists a variety of deep learning based segmentation techniques.…”

Section: Motivationmentioning

confidence: 99%

Understanding Deep Learning Techniques for Image Segmentation

et al. 2019

View full text Add to dashboard Cite

The machine learning community has been overwhelmed by a plethora of deep learning based approaches. Many challenging computer vision tasks such as detection, localization, recognition and segmentation of objects in unconstrained environment are being efficiently addressed by various types of deep neural networks like convolutional neural networks, recurrent networks, adversarial networks, autoencoders and so on. While there have been plenty of analytical studies regarding the object detection or recognition domain, many new deep learning techniques have surfaced with respect to image segmentation techniques. This paper approaches these various deep learning techniques of image segmentation from an analytical perspective. The main goal of this work is to provide an intuitive understanding of the major techniques that has made significant contribution to the image segmentation domain. Starting from some of the traditional image segmentation approaches, the paper progresses describing the effect deep learning had on the image segmentation domain. Thereafter, most of the major segmentation algorithms have been logically categorized with paragraphs dedicated to their unique contribution. With an ample amount of intuitive explanations, the reader is expected to have an improved ability to visualize the internal dynamics of these processes.

show abstract

“…DNNs, such as Convolutional Neural Networks (CNNs), powerful for computer vision tasks [13], Recurrent Neural Networks (RNNs), for natural language processing, are all generally layer-centric, hierarchically organized models containing multiple types of layers. These layers can be categorized [20] as below:…”

Section: Layer-centric Training Processmentioning

confidence: 99%

Layer-Centric Memory Reuse and Data Migration for Extreme-Scale Deep Learning on Many-Core Architectures

Jin

Liu

Jiang

et al. 2018

ACM Trans. Archit. Code Optim.

View full text Add to dashboard Cite

Due to the popularity of Deep Neural Network (DNN) models, we have witnessed extreme-scale DNN models with the continued increase of the scale in terms of depth and width. However, the extremely high memory requirements for them make it difficult to run the training processes on single many-core architectures such as a Graphic Processing Unit (GPU), which compels researchers to use model parallelism over multiple GPUs to make it work. However, model parallelism always brings very heavy additional overhead. Therefore, running an extreme-scale model in a single GPU is urgently required. There still exist several challenges to reduce the memory footprint for extreme-scale deep learning. To address this tough problem, we first identify the memory usage characteristics for deep and wide convolutional networks, and demonstrate the opportunities for memory reuse at both the intra-layer and inter-layer levels. We then present Layrub, a runtime data placement strategy that orchestrates the execution of the training process. It achieves layer-centric reuse to reduce memory consumption for extreme-scale deep learning that could not previously be run on a single GPU. Experiments show that, compared to the original Caffe, Layrub can cut down the memory usage rate by an average of 58.2% and by up to 98.9%, at the moderate cost of 24.1% higher training execution time on average. Results also show that Layrub outperforms some popular deep learning systems such as GeePS, vDNN, MXNet, and Tensorflow. More importantly, Layrub can tackle extreme-scale deep learning tasks. For example, it makes an extra-deep ResNet with 1,517 layers that can be trained successfully in one GPU with 12GB memory, while other existing deep learning systems cannot.

show abstract

Survey of recent progress in semantic image segmentation with CNNs

Cited by 66 publications

References 63 publications

Machine Learning‐Enabled Smart Sensor Systems

Machine Learning‐Enabled Smart Sensor Systems

Understanding Deep Learning Techniques for Image Segmentation

Layer-Centric Memory Reuse and Data Migration for Extreme-Scale Deep Learning on Many-Core Architectures

Contact Info

Product

Resources

About