scholarly journals Visual saliency-based bas-relief generation with symmetry composition rule

2018 ◽  
Vol 29 (3-4) ◽  
pp. e1815
Author(s):  
Tingting Li ◽  
Liying Yang ◽  
Meili Wang ◽  
Yuling Fan ◽  
Feiyu Zhang ◽  
...  
Keyword(s):  
Visual Saliency ◽  
Composition Rule

Visual Saliency Detection: An Information Theoretic Algorithm Combined Long-term with Short-term Features

2014 ◽  
Vol 35 (7) ◽  
pp. 1636-1643
Author(s):  
Xiao-liang Qian ◽  
Lei Guo ◽  
Jun-wei Han ◽  
Xin-tao Hu ◽  
Gong Cheng
Keyword(s):  
Saliency Detection ◽  
Visual Saliency ◽  
Short Term ◽  
Information Theoretic ◽  
Visual Saliency Detection

scholarly journals Guided Spatial Transformers for Facial Expression Recognition

2021 ◽  
Vol 11 (16) ◽  
pp. 7217
Author(s):  
Cristina Luna-Jiménez ◽  
Jorge Cristóbal-Martín ◽  
Ricardo Kleinlein ◽  
Manuel Gil-Martín ◽  
José M. Moya ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expert Knowledge ◽  
Visual Saliency ◽  
Expression Recognition ◽  
Spatial Transformations ◽  
Facial Landmarks ◽  
Saliency Maps ◽  
Best Fit

Spatial Transformer Networks are considered a powerful algorithm to learn the main areas of an image, but still, they could be more efficient by receiving images with embedded expert knowledge. This paper aims to improve the performance of conventional Spatial Transformers when applied to Facial Expression Recognition. Based on the Spatial Transformers’ capacity of spatial manipulation within networks, we propose different extensions to these models where effective attentional regions are captured employing facial landmarks or facial visual saliency maps. This specific attentional information is then hardcoded to guide the Spatial Transformers to learn the spatial transformations that best fit the proposed regions for better recognition results. For this study, we use two datasets: AffectNet and FER-2013. For AffectNet, we achieve a 0.35% point absolute improvement relative to the traditional Spatial Transformer, whereas for FER-2013, our solution gets an increase of 1.49% when models are fine-tuned with the Affectnet pre-trained weights.

Lighter and Faster Cross-Concatenated Multi-Scale Residual Block Based Network for Visual Saliency Prediction

2021 ◽  
Author(s):  
Sai Phani Kumar Malladi ◽  
Jayanta Mukhopadhyay ◽  
Chaker Larabi ◽  
Santanu Chaudhury
Keyword(s):  
Visual Saliency ◽  
Multi Scale ◽  
Saliency Prediction ◽  
Block Based ◽  
Residual Block

An adaptive enhancement algorithm based on visual saliency for low illumination images

2021 ◽  
Author(s):  
Shenyi Qian ◽  
Yongsheng Shi ◽  
Huaiguang Wu ◽  
Jinhua Liu ◽  
Weiwei Zhang
Keyword(s):  
Visual Saliency ◽  
Adaptive Enhancement ◽  
Low Illumination ◽  
Enhancement Algorithm

scholarly journals Cuticle segmentation based on visual saliency

2021 ◽  
Vol 647 ◽  
pp. 012183
Author(s):  
Wenxia Zhang ◽  
Chunguang Wang ◽  
Haichao Wang ◽  
Xiaofei Yin
Keyword(s):  
Visual Saliency

scholarly journals Understanding Visual Saliency in Mobile User Interfaces

2020 ◽  
Author(s):  
Luis A. Leiva ◽  
Yunfei Xue ◽  
Avya Bansal ◽  
Hamed R. Tavakoli ◽  
Tuðçe Köroðlu ◽  
...  
Keyword(s):  
User Interfaces ◽  
Visual Saliency ◽  
Mobile User

Deep Audio-Visual Saliency: Baseline Model and Data

2020 ◽  
Author(s):  
Hamed Rezazadegan Tavakoli ◽  
Ali Borji ◽  
Juho Kannala ◽  
Esa Rahtu
Keyword(s):  
Visual Saliency ◽  
Baseline Model
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