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Multi-Scale Dense Networks for Deep High Dynamic Range Imaging

Qingsen Yan, Dong Gong, Pingping Zhang, Qinfeng Shi, Jinqiu Sun, Ian Reid, Yanning Zhang
2019 2019 IEEE Winter Conference on Applications of Computer Vision (WACV)  
Multi-scale dense networks for deep high dynamic range imaging  ...  These images are feed into the multi-scale dense networks to generate the multi-scale HDR images. Afterwards, we utilize a shallow network to refine the multi-scale predictions.  ...  Conclusions In this paper, we present a multi-scale dense network to generate HDR images.  ... 
doi:10.1109/wacv.2019.00012 dblp:conf/wacv/YanGZSSRZ19 fatcat:fooutalrgfdbbkx45plwuhkql4

Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking [article]

Jianfeng Yan, Zizhuang Wei, Hongwei Yi, Mingyu Ding, Runze Zhang, Yisong Chen, Guoping Wang, Yu-Wing Tai
2020 arXiv   pre-print
In this paper, we propose an efficient and effective dense hybrid recurrent multi-view stereo net with dynamic consistency checking, namely D^2HC-RMVSNet, for accurate dense point cloud reconstruction.  ...  Our novel hybrid recurrent multi-view stereo net consists of two core modules: 1) a light DRENet (Dense Reception Expanded) module to extract dense feature maps of original size with multi-scale context  ...  Dynamic Consistency Checking The above DH-RMVSNet generates dense pixel-wise depth map for each input multi-view images.  ... 
arXiv:2007.10872v1 fatcat:m2qqs7ad75c63i5ud2v4ofulfe

Aerial multi-object tracking by detection using deep association networks [article]

Ajit Jadhav, Prerana Mukherjee, Vinay Kaushik, Brejesh Lall
2019 arXiv   pre-print
Using this architecture for object detection, we build a custom DeepSORT network for object detection on the VisDrone2019 MOT dataset by training a custom Deep Association network for the algorithm.  ...  Inspite of the existing research, these algorithms are not usually optimal for dealing with sequences or images captured by drone-based platforms, due to various challenges such as view point change, scales  ...  high scale and variablitiy of the images in the VisDrone dataset.  ... 
arXiv:1909.01547v1 fatcat:b3xnyd6fprae5cr73tnp33xjma

2019 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 29

2019 IEEE transactions on circuits and systems for video technology (Print)  
An Adaptive Quantizer for High Dynamic Range Content: Application to Video Coding.  ...  ., +, TCSVT Aug. 2019 2376-2390 Multi-Scale Attention Deep Neural Network for Fast Accurate Object Detec- tion.  ... 
doi:10.1109/tcsvt.2019.2959179 fatcat:2bdmsygnonfjnmnvmb72c63tja

Moire Image Restoration using Multi Level Hyper Vision Net [article]

D.Sabari Nathan and M.Parisa Beham and S. M. Md Mansoor Roomi
2020 arXiv   pre-print
A moire pattern in the images is resulting from high frequency patterns captured by the image sensor (colour filter array) that appear after demosaicing.  ...  These Moire patterns would appear in natural images of scenes with high frequency content. The Moire pattern can also vary intensely due to a minimal change in the camera direction/positioning.  ...  The convolutional layers and the residual dense attention blocks are utilized for better performance and to retain the multi-scale information.  ... 
arXiv:2004.08541v1 fatcat:e4emtpphc5cqdfqkt7ifrh7l6y

ACNet: Mask-Aware Attention with Dynamic Context Enhancement for Robust Acne Detection [article]

Kyungseo Min, Gun-Hee Lee, Seong-Whan Lee
2021 arXiv   pre-print
Then, Dynamic Context Enhancement controls different receptive fields of multi-scale features for context enhancement to handle scale variation.  ...  Although deep learning played a major role in the recent success of acne detection, there are still several challenges such as color shift by inconsistent illumination, variation in scales, and high density  ...  variation, we introduce Dynamic Context Enhancement that dynamically controls receptive fields of multi-scale features. • For dense and small objects, we develop Mask-Aware Multi-Attention to diminish  ... 
arXiv:2105.14891v3 fatcat:j64nn46o4zgcbisezorz6wiuze

Mesoscopic Facial Geometry Inference Using Deep Neural Networks

Loc Huynh, Weikai Chen, Shunsuke Saito, Jun Xing, Koki Nagano, Andrew Jones, Paul Debevec, Hao Li
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
Abstract We present a learning-based approach for synthesizing facial geometry at medium and fine scales from diffusely-lit facial texture maps.  ...  Instead of directly inferring 3D geometry, we propose to encode fine details in high-resolution displacement maps which are learned through a hybrid network adopting the state-of-the-art image-to-image  ...  Government is authorized to reproduce and distribute reprints for Governmental purpose notwithstanding any copyright annotation thereon.  ... 
doi:10.1109/cvpr.2018.00877 dblp:conf/cvpr/Huynh0SXN0DL18 fatcat:sla2zupg4nbdfddm5cmbcry3ae

ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-Resolution [article]

Jialiang Shen, Yucheng Wang, Jian Zhang
2020 arXiv   pre-print
To obtain a more computationally efficient model for arbitrary scale SR, this paper employs a Laplacian pyramid method to reconstruct any-scale high-resolution (HR) images using the high-frequency image  ...  Deep convolutional neural networks have significantly improved the peak signal-to-noise ratio of SuperResolution (SR).  ...  In this paper, we propose our network as Any-Scale Deep Super-Resolution Network (ASDN) based on the multi-scale parallel reconstruction architecture.  ... 
arXiv:2010.02414v1 fatcat:zvn4hsd7vra5biet6eg55vgqfi

2021 Index IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 43

2022 IEEE Transactions on Pattern Analysis and Machine Intelligence  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TPAMI July 2021 2206-2219 High Speed and High Dynamic Range Video with an Event Camera.  ...  ., +, TPAMI July 2021 2206-2219 High Speed and High Dynamic Range Video with an Event Camera.  ... 
doi:10.1109/tpami.2021.3126216 fatcat:h6bdbf2tdngefjgj76cudpoyia

Multi-Scale Dense Networks for Resource Efficient Image Classification [article]

Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, Kilian Q. Weinberger
2018 arXiv   pre-print
To facilitate high quality classification early on, we use a two-dimensional multi-scale network architecture that maintains coarse and fine level features all-throughout the network.  ...  To maximally re-use computation between the classifiers, we incorporate them as early-exits into a single deep convolutional neural network and inter-connect them with dense connectivity.  ...  We are also thankful for generous support by SAP America Inc.  ... 
arXiv:1703.09844v5 fatcat:kgrj6pahbbahfocsfwdpdnyc54

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 1016-1029 Deep Collaborative Multi-View Hashing for Large-Scale Image Search. Zhu, L., +, TIP 2020 4643-4655 Deep HDR Imaging via A Non-Local Network.  ...  ., +, TIP 2020 5396-5407 Multi-Scale Multi-View Deep Feature Aggregation for Food Recognition.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Learning Channel Inter-dependencies at Multiple Scales on Dense Networks for Face Recognition [article]

Qiangchang Wang, Guodong Guo, Mohammad Iqbal Nouyed
2019 arXiv   pre-print
Inspired by recent progress in deep networks, we consider some important concepts, including multi-scale feature learning, dense connections of network layers, and weighting different network flows, for  ...  We propose a new deep network structure for unconstrained face recognition.  ...  Conclusion We have developed a new network structure for deep learning, based on the integration of multi-scale feature learning, dense connections of layers, and correlating and weighting different network  ... 
arXiv:1711.10103v2 fatcat:vejhk3xsubbnlh5hmte37m3tcq

A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks [article]

Baichuan Huang, Jun Zhao, Jingbin Liu
2020 arXiv   pre-print
The contributions of this paper can be summarized as follows: the paper provides a high quality and full-scale overview in SLAM.  ...  For Lidar and visual fused SLAM, the paper highlights the multi-sensors calibration, the fusion in hardware, data, task layer.  ...  Hence, event cameras can performance better than traditional camera in high speed and high dynamic range.  ... 
arXiv:1909.05214v4 fatcat:itnluvkewfd6fel7x65wdgig3e

An Overview of Perception and Decision-Making in Autonomous Systems in the Era of Learning [article]

Yang Tang, Chaoqiang Zhao, Jianrui Wang, Chongzhen Zhang, Qiyu Sun, Weixing Zheng, Wenli Du, Feng Qian, Juergen Kurths
2020 arXiv   pre-print
First, we delineate the existing classical simultaneous localization and mapping (SLAM) solutions and review the environmental perception and understanding methods based on deep learning, including deep  ...  learning-based monocular depth estimation, ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional SLAM frameworks.  ...  [169] used deep neural networks to enhance the brightness constancy of image sequences captured from high dynamic range (HDR) environments.  ... 
arXiv:2001.02319v3 fatcat:z3zhp2cyonfqtlttl2y57572uy

Special Section Guest Editorial: Change Detection Using Multi-Source Remotely Sensed Imagery

Xin Huang, Jiayi Li, Francesca Bovolo, Qi Wang
2019 Remote Sensing  
This special issue hosts papers on change detection technologies and analysis in remote sensing, including multi-source sensors, advanced machine learning technologies for change information mining, and  ...  The presented results showed improved results when multi-source remote sensed data was used in change detection.  ...  Acknowledgments: The Guest Editors would like to thank the authors who contributed to this Special Issue for sharing their scientific researches and for their excellent collaboration.  ... 
doi:10.3390/rs11192216 fatcat:ddzetus3lvfslc7u3yu4bscnme
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