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A Comparative Study of Real-Time Semantic Segmentation for Autonomous Driving

Mennatullah Siam, Mostafa Gamal, Moemen Abdel-Razek, Senthil Yogamani, Martin Jagersand, Hong Zhang
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Most of the research on semantic segmentation is focused on improving the accuracy with less attention paid to computationally efficient solutions.  ...  We performed detailed experimental analysis on cityscapes dataset for various combinations of encoder and decoder.  ...  Little attention is given to the computational efficiency of deep neural networks for semantic segmentation.  ... 
doi:10.1109/cvprw.2018.00101 dblp:conf/cvpr/SiamGAYJ018 fatcat:4dq7v7fo55elppdw2iv3akwzou

Intelligent Video Analytics For Human Action Detection: A Deep Learning Approach With Transfer Learning

Saylee Begampure, Parul Jadhav
2022 International Journal of Computing and Digital Systems  
This method can be further implemented for on edge processing in embedded platforms for real time applications.  ...  The pre-processing technique of redundant frame detection along with pre-trained Convolutional Neural Network (CNN) is implemented for classifying the activities efficiently.  ...  learning approach on Inception Challenge faced was of high-speed parallel processing architecture as data was in video form, in future video data can be compressed, localized and unwanted part can be  ... 
doi:10.12785/ijcds/110105 fatcat:npanedtfove2rd2c37bqalncmy

Deep Learning Post-Filtering Using Multi-Head Attention and Multiresolution Feature Fusion for Image and Intra-Video Quality Enhancement

Ionut Schiopu, Adrian Munteanu
2022 Sensors  
The paper proposes a novel post-filtering method based on convolutional neural networks (CNNs) for quality enhancement of RGB/grayscale images and video sequences.  ...  The proposed architecture is built using a set of efficient processing blocks designed based on the following concepts: (i) the multi-head attention mechanism for refining the feature maps, (ii) the weight  ...  In this work, we propose a novel filtering method based on convolutional neural networks (CNNs) designed to enhance the quality of high-resolution images and video sequences by post-processing the decoded  ... 
doi:10.3390/s22041353 pmid:35214252 pmcid:PMC8963040 fatcat:gal4lnc4mrfibca4vqpb2biwgq

TensorFlow Enabled Deep Learning Model Optimization for enhanced Realtime Person Detection using Raspberry Pi operating at the Edge

Reenu Mohandas, Mangolika Bhattacharya, Mihai Penica, Karl Van Camp, Martin J. Hayes
2020 Irish Conference on Artificial Intelligence and Cognitive Science  
In this paper Quantization effects are assessed for a real time Edge based person detection use case that is based on the use of a Raspberry Pi.  ...  deployed on the Pi for realtime applications.  ...  For those deep learning models that rely on dense layers, the number of parameters can number in the billions [7] .  ... 
dblp:conf/aics/MohandasBPCH20 fatcat:5xa3x5zwozckfohem4atay7mgi

Converting Optical Videos to Infrared Videos Using Attention GAN and Its Impact on Target Detection and Classification Performance

Mohammad Shahab Uddin, Reshad Hoque, Kazi Aminul Islam, Chiman Kwan, David Gribben, Jiang Li
2021 Remote Sensing  
The basic idea is to focus on target areas using attention generative adversarial network (attention GAN), which will preserve the fidelity of target areas.  ...  To apply powerful deep-learning-based algorithms for object detection and classification in infrared videos, it is necessary to have more training data in order to build high-performance models.  ...  There are two types of attention GAN models in the literature for image-to-image translation: self-attention-based GAN model [25, 26] and teacher-attention-based GAN model [24] .  ... 
doi:10.3390/rs13163257 doaj:eb138c0cacbc44e6b5ac924b96251c53 fatcat:m526vnbkirbgbi2snypwca4mna

Exploiting Attention-Consistency Loss For Spatial-Temporal Stream Action Recognition

Haotian Xu, Xiaobo Jin, Qiufeng Wang, Amir Hussain, Kaizhu Huang
2022 ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)  
Specifically, a branch-independent convolutional neural network (CNN) based algorithm is developed with a novel attention-consistency loss metric, enabling the temporal stream to concentrate on consistent  ...  We evaluate the proposed method for action recognition on two benchmark datasets: Kinetics400 and UCF101.  ...  Therefore, we design a post-processing module to generate the spatial CAM to better describe the attention of spatial streams.  ... 
doi:10.1145/3538749 fatcat:v6rqyknkdvajfa2b4hcirl74ue

Survey on Semantic Segmentation using Deep Learning Techniques

Fahad Lateef, Yassine Ruichek
2019 Neurocomputing  
For this reason, we propose to survey these methods by, first categorizing them into ten different classes according to the common concepts underlying their architectures.  ...  Moreover, we focus on some of the methods and look closely at their architectures in order to find out how they have achieved their reported performances.  ...  ACKNOWLEDGMENT The authors express their gratitude to University Technology Belfort-Montbeliard and Higher Education Commission of Pakistan for providing the support and necessary requirement for completion  ... 
doi:10.1016/j.neucom.2019.02.003 fatcat:aelsfl7unvdw5j2rtyqhtgqrsm

Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches [article]

Omar Elharrouss, Younes Akbari, Noor Almaadeed, Somaya Al-Maadeed
2022 arXiv   pre-print
In addition, a comparison in terms of performance is also provided, based on the backbone used for each task.  ...  CNNs allow to work on large-scale size of data, as well as cover different scenarios for a specific task.  ...  BN is applied to the Inception network (BN-Inception 6 ) and tested on ImageNet for image classification [19] .  ... 
arXiv:2206.08016v1 fatcat:qxfezmrhsvgj7jmazbh5uctlum

Camera-based discomfort detection using multi-channel attention 3D-CNN for hospitalized infants

Yue Sun, Jingjing Hu, Wenjin Wang, Min He, Peter H. N. de With
2021 Quantitative Imaging in Medicine and Surgery  
In this paper, we present an automatic and continuous video-based system for monitoring and detecting discomfort in infants.  ...  The realized improvements of the 3D-CNN are based on capturing both the motion and the facial expression information of the infants.  ...  For each infant in the database, written consent was obtained from the parents.  ... 
doi:10.21037/qims-20-1302 pmid:34249635 pmcid:PMC8250023 fatcat:pm2srgp24bgidg5i46rjpnqhty

Image Captioning Algorithm Based on Multi-Branch CNN and Bi-LSTM

Shan HE, Yuanyao LU, Shengnan CHEN
2021 IEICE transactions on information and systems  
The development of deep learning and neural networks has brought broad prospects to computer vision and natural language processing.  ...  We conducted experiments on Flickr8k, Flickr30k and MSCOCO datasets.  ...  Computer vision involves processing and analyzing digital data including images and videos.  ... 
doi:10.1587/transinf.2020edp7227 fatcat:45tq63dubffjrey47fkrlk6y6u

Review of Image Classification Algorithms Based on Convolutional Neural Networks

Leiyu Chen, Shaobo Li, Qiang Bai, Jing Yang, Sanlong Jiang, Yanming Miao
2021 Remote Sensing  
Convolutional neural networks (CNNs) have gradually become the mainstream algorithm for image classification since 2012, and the CNN architecture applied to other visual recognition tasks (such as object  ...  In this review, which focuses on the application of CNNs to image classification tasks, we cover their development, from their predecessors up to recent state-of-the-art (SOAT) network architectures.  ...  Vision Transformer Transformers [217] based on self-attention mechanism [218] have achieved great success in natural language processing (NLP).  ... 
doi:10.3390/rs13224712 fatcat:vwo6no6bl5dnnpelfkm2k4scpe

A CNN-based Prediction-Aware Quality Enhancement Framework for VVC

Fatemeh Nasiri, Wassim Hamidouche, Luce Morin, Nicolas Dhollande, Gildas Cocherel
2021 IEEE Open Journal of Signal Processing  
This paper presents a framework for Convolutional Neural Network (CNN)-based quality enhancement task, by taking advantage of coding information in the compressed video signal.  ...  The proposed CNN-based Quality Enhancement (QE) framework has been implemented on top of the Versatile Video Coding (VVC) Test Model (VTM-10).  ...  DIANet PCS 19 [44] QP ILF Dense inception net. with different attention blocks, separating inter/intra frames. - IEEE-TIP 19 [45] BSDS500 QP ILF Content-aware ILF with adaptive network selection depending  ... 
doi:10.1109/ojsp.2021.3092598 fatcat:dqgzponwuza7taqsjalo36rgby

ActivityNet Challenge 2017 Summary [article]

Bernard Ghanem, Juan Carlos Niebles, Cees Snoek, Fabian Caba Heilbron, Humam Alwassel, Ranjay Khrisna, Victor Escorcia, Kenji Hata, Shyamal Buch
2017 arXiv   pre-print
We would like to thank the authors of the Kinetics dataset for their kind support; and Joao Carreira and Brian Zhang for helpful discussions.  ...  The approach in [30] also uses handcrafted motion features like MBH on top of inception and C3D features in addition to dynamic programing based post processing.  ...  For CNN feature, firstly, we train the inception-v1 network on the ImageNet 21k dataset.  ... 
arXiv:1710.08011v1 fatcat:bc5qhp2cungrdj4j3lebxeoane

Table of Contents

2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
With Occlusion Handling for Panoptic Segmentation Cross-View Correspondence Reasoning Based on Bipartite Graph Convolutional Network for Mammogram Mass Detection 3811 Yuhang Liu (Deepwise AI Lab), Fandong  ...  Learned Video Compression 3543 Jianping Lin (CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, University of Science and Technology of China, Hefei, China)  ... 
doi:10.1109/cvpr42600.2020.00004 fatcat:c7els2kee5cq7lh6cemeqhdcoa

NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results [article]

Dario Fuoli, Zhiwu Huang, Martin Danelljan, Radu Timofte, Hua Wang, Longcun Jin, Dewei Su, Jing Liu, Jaehoon Lee, Michal Kudelski, Lukasz Bala, Dmitry Hrybov (+9 others)
2020 arXiv   pre-print
In particular, track 1 offers a new Internet video benchmark, requiring algorithms to learn the map from more compressed videos to less compressed videos in a supervised training manner.  ...  This paper reviews the NTIRE 2020 challenge on video quality mapping (VQM), which addresses the issues of quality mapping from source video domain to target video domain.  ...  [9] suggests a post-processing algorithm for artifact reduction on compressed videos.  ... 
arXiv:2005.02291v3 fatcat:z5zgwpnyrveothp337xeb7yfoy
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