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Deep Green Function Convolution for Improving Saliency in Convolutional Neural Networks
[article]
2019
arXiv
pre-print
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The objective of this paper is to show that saliency convolutional neural networks (CNN) can be improved by using a Green's function convolution (GFC) to extrapolate edges features into salient regions ...
A major contribution of the current work is the first implementation of Green's function convolution inside a neural network, which allows the network to operate in the feature domain and in the gradient ...
Then, the Green function in the Fourier domain mono ℱ is given by (8) [20] . In equation (8) , mono ℱ is the Green's function that allows to solve any Laplacian by a convolution [20, 29] . ...
arXiv:1908.08331v2
fatcat:evoqhkzgibdp3bchxnxpffw2we
Understanding Saliency Prediction with Deep Convolutional Neural Networks and Psychophysical Models
[article]
2022
arXiv
pre-print
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Convolutional neural networks (CNNs) have achieved great success in natural image saliency prediction. ...
However, psychophysical models were more unstable in noise than pre-trained deep neural networks. ...
It can improve the generality and stability of artificial neural networks, but there is insufficient research in this area. ...
arXiv:2204.06071v3
fatcat:mvddbj3sszfuvnwo23adanau34
Salient Object Detection in Video using Deep Non-Local Neural Networks
[article]
2018
arXiv
pre-print
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A novel deep non-local neural network architecture is introduced for video salient object detection and tested on two well-known datasets DAVIS and FBMS. ...
In spite of the fact that the state of the art in saliency detection for still images has been changed substantially over the last few years, there have been few improvements in video saliency detection ...
The re-emergence of convolutional neural networks (CNN) has brought about a signif-icant improvement in a wide range of computer vision areas. ...
arXiv:1810.07097v1
fatcat:usc6h26hvnfphohkzeamrwopxe
Deep Contrast Learning for Salient Object Detection
[article]
2016
arXiv
pre-print
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Salient object detection has recently witnessed substantial progress due to powerful features extracted using deep convolutional neural networks (CNNs). ...
In this CVPR 2016 paper, we propose an end-to-end deep contrast network to overcome the aforementioned limitations. ...
[25] trained a deep neural network for deriving a saliency map from multiscale features extracted using deep convolutional neural networks. Wang et al. ...
arXiv:1603.01976v1
fatcat:6fzzvx7bbzchnoareblukqe2be
A Fast and Compact Saliency Score Regression Network Based on Fully Convolutional Network
[article]
2017
arXiv
pre-print
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In this paper, we tackle this problem by proposing a fast and compact saliency score regression network which employs fully convolutional network, a special deep convolutional neural network, to estimate ...
It is an extremely simplified end-to-end deep neural network without any pre-processings and post-processings. ...
natural to consider convolutional neural networks for saliency detection. ...
arXiv:1702.00615v2
fatcat:k5cmdlt6m5audhjm4yzun52dum
Contrast-Oriented Deep Neural Networks for Salient Object Detection
2018
IEEE Transactions on Neural Networks and Learning Systems
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Deep convolutional neural networks have become a key element in the recent breakthrough of salient object detection. ...
Each of our deep networks is composed of two complementary components, including a fully convolutional stream for dense prediction and a segment-level spatial pooling stream for sparse saliency inference ...
DEEP CONTRAST NETWORK As illustrated in Fig. 1 , our proposed contrast-oriented deep neural network is composed of two complementary components, a fully convolutional stream for dense saliency prediction ...
doi:10.1109/tnnls.2018.2817540
pmid:29993934
fatcat:fvchrcr6sfclfgmimhg75otw6u
Deep Contrast Learning for Salient Object Detection
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Salient object detection has recently witnessed substantial progress due to powerful features extracted using deep convolutional neural networks (CNNs). ...
In this paper, we propose an end-to-end deep contrast network to overcome the aforementioned limitations. ...
[26] trained a deep neural network for deriving a saliency map from multiscale features extracted using deep convolutional neural networks. Wang et al. ...
doi:10.1109/cvpr.2016.58
dblp:conf/cvpr/LiY16
fatcat:ujctasozxnbj5kr3cfz6bi3e7y
Self-Attention Recurrent Network for Saliency Detection
[article]
2018
arXiv
pre-print
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Feature maps in deep neural network generally contain different semantics. Existing methods often omit their characteristics that may lead to sub-optimal results. ...
In this paper, we propose a novel end-to-end deep saliency network which could effectively utilize multi-scale feature maps according to their characteristics. ...
In summary, contributions of this paper are as followings: 1. We propose a novel end-to-end deep convolutional neural network for robust saliency detection. The network consists of two parts. ...
arXiv:1808.01634v1
fatcat:uk4mdoefyfashovg6re6fulu6y
Improving the Curvelet Saliency and Deep Convolutional Neural Networks for Diabetic Retinopathy Classification in Fundus Images
2022
Engineering, Technology & Applied Science Research
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This paper proposes a new method to classify diabetic retinopathy in retinal blood vessel images based on curvelet saliency for segmentation. ...
To evaluate the results of the proposed method STARE and HRF datasets are used for testing with the Jaccard Index. ...
www.etasr.com Tuyet et al.: Improving the Curvelet Saliency with Deep Convolutional Neural Networks for Diabetic …
Fig. 4 . 4 Fig. 4. Segmentation result comparison in the HRF dataset. ...
doi:10.48084/etasr.4679
fatcat:tinmiwbvfzeflgay2nkzn74434
Saliency Maps-Based Convolutional Neural Networks for Facial Expression Recognition
2021
IEEE Access
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INDEX TERMS Facial expression recognition, saliency maps, dilated convolution, prior knowledge, the convolutional neural network. ...
With the prior knowledge of saliency maps and the multilayer deep features in the CNN network, the recognition accuracy is improved by obtaining more targeted and more complete deep expression information ...
In recent years, the convolutional neural network (CNN) [3] model is one of the most popular methods used for FER tasks [4] . ...
doi:10.1109/access.2021.3082694
fatcat:kedvbskwy5ellbm3eq4p3vuyla
Estimating Attention of Faces Due to its Growing Level of Emotions
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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The concept of deep convolution neural network (CNN) has been applied for training and classification of different facial expression of emotions. ...
In the literature of visual saliency authors have dealt with expressionless objects but it has not been addressed with object like face which exploits expressions. ...
This is computed by deep convolution neural network (described in section 3 and 4). ...
doi:10.1109/cvprw.2018.00261
dblp:conf/cvpr/KumarGKS18
fatcat:3bv2dnzyzfdkrnpedjfdoljxe4
Visualization of Salient Object with Saliency Maps using Residual Neural Networks
2021
IEEE Access
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"Deepfix: A fully convolutional neural network for predicting human eye fixations." ...
INTRODUCTION The field of computer vision has taken a sensational curve, with the ascent of the Convolutional Neural Networks (CNNs), which is one of the most impressive form of Artificial Neural Networks ...
doi:10.1109/access.2021.3100155
fatcat:aqzrky7vhfbfbpwazj4fva2w2q
Pyramid Spatial Context Features for Salient Object Detection
2020
IEEE Access
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By further inserting this module in a deep network, namely PSCNet, we are able to optimize the network in an end-to-end manner for salient object detection. ...
This paper presents a novel deep neural network design for salient object detection by formulating a pyramid spatial context module, PSC module for short, to capture the spatial context information at ...
Next, we insert the PSC module in a deep convolutional neural network to build the PSCNet for salient object detection. ...
doi:10.1109/access.2020.2993572
fatcat:rigrimruubhbznb54ka4hoyb74
Convolutional Edge Constraint-Based U-Net for Salient Object Detection
2019
IEEE Access
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Besides, the convolutional neural network (CNN)-based models predict saliency maps at patch scale, which causes the objects edges of the output to be fuzzy. ...
Moreover, in order to better guide the network mining the information of objects edges, we design a new loss function based on image convolution, which adds an L1 constraint to the edge information of ...
ACKNOWLEDGMENT This paper was presented in part at the Chinese Conference on Pattern Recognition and Computer Vision, Guangzhou, 2018. This paper was recommended by the program committee. ...
doi:10.1109/access.2019.2910572
fatcat:ottfbvrhxjahfez4j35ns4qbve
On Combining DeepSnake and Global Saliency for Detection of Orchard Apples
2021
Applied Sciences
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fruit regions in the saliency map. ...
For the fast detection and recognition of apple fruit targets, based on the real-time DeepSnake deep learning instance segmentation model, this paper provided an algorithm basis for the practical application ...
deep neural network, the data of each mini-bacth in the network normalized processing. ...
doi:10.3390/app11146269
fatcat:awvgswnwwzeuzogvtpbnyqcgqq
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