scholarly journals Feature Pyramid Attention Model and Multi-Label Focal Loss for Pedestrian Attribute Recognition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 164570-164579
Author(s):  
Ye Li ◽  
Fangyan Shi ◽  
Shaoqi Hou ◽  
Jipeng Li ◽  
Chao Li ◽  
...  
Keyword(s):  
Attention Model ◽  
Feature Pyramid ◽  
Attribute Recognition

scholarly journals Recurrent Attention Model for Pedestrian Attribute Recognition

2019 ◽  
Vol 33 ◽  
pp. 9275-9282 ◽  
Author(s):  
Xin Zhao ◽  
Liufang Sang ◽  
Guiguang Ding ◽  
Jungong Han ◽  
Na Di ◽  
...  
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Region Of Interest ◽  
Training Dataset ◽  
Learning Context ◽  
Feature Map ◽  
Attention Model ◽  
Spatial Locality ◽  
Attribute Recognition ◽  
Convolutional Lstm

Pedestrian attribute recognition is to predict attribute labels of pedestrian from surveillance images, which is a very challenging task for computer vision due to poor imaging quality and small training dataset. It is observed that many semantic pedestrian attributes to be recognised tend to show spatial locality and semantic correlations by which they can be grouped while previous works mostly ignore this phenomenon. Inspired by Recurrent Neural Network (RNN)’s super capability of learning context correlations and Attention Model’s capability of highlighting the region of interest on feature map, this paper proposes end-to-end Recurrent Convolutional (RC) and Recurrent Attention (RA) models, which are complementary to each other. RC model mines the correlations among different attribute groups with convolutional LSTM unit, while RA model takes advantage of the intra-group spatial locality and inter-group attention correlation to improve the performance of pedestrian attribute recognition. Our RA method combines the Recurrent Learning and Attention Model to highlight the spatial position on feature map and mine the attention correlations among different attribute groups to obtain more precise attention. Extensive empirical evidence shows that our recurrent model frameworks achieve state-of-the-art results, based on pedestrian attribute datasets, i.e. standard PETA and RAP datasets.

scholarly journals Skeleton-Based Attention Mask for Pedestrian Attribute Recognition Network

2021 ◽  
Vol 7 (12) ◽  
pp. 264
Author(s):  
Sorn Sooksatra ◽  
Sitapa Rujikietgumjorn
Keyword(s):  
Data Augmentation ◽  
Classification Performance ◽  
Experimental Result ◽  
Whole Body ◽  
Target Area ◽  
Extended Model ◽  
Attention Model ◽  
Backbone Networks ◽  
Body Regions ◽  
Attribute Recognition

This paper presents an extended model for a pedestrian attribute recognition network utilizing skeleton data as a soft attention model to extract a local feature corresponding to a specific attribute. This technique helped keep valuable information surrounding the target area and handle the variation of human posture. The attention masks were designed to focus on the partial and the whole-body regions. This research utilized an augmented layer for data augmentation inside the network to reduce over-fitting errors. Our network was evaluated in two datasets (RAP and PETA) with various backbone networks (ResNet-50, Inception V3, and Inception-ResNet V2). The experimental result shows that our network improves overall classification performance with a mean accuracy of about 2–3% in the same backbone network, especially local attributes and various human postures.

Fast Visual Attention Model Algorithm Based on Approximate Gaussian Pyramids

2009 ◽  
Vol 20 (12) ◽  
pp. 3240-3253 ◽  
Author(s):  
Guo-Min ZHANG ◽  
Jian-Ping YIN ◽  
En ZHU ◽  
Ling MAO
Keyword(s):  
Visual Attention ◽  
Visual Attention Model ◽  
Attention Model ◽  
Model Algorithm

Feedback Attention Model for Image Captioning

2019 ◽  
Vol 31 (7) ◽  
pp. 1122
Author(s):  
Fan Lyu ◽  
Fuyuan Hu ◽  
Yanning Zhang ◽  
Zhenping Xia ◽  
S Sheng Victor
Keyword(s):  
Image Captioning ◽  
Attention Model

scholarly journals Mask-R$$^{2}$$CNN: a distance-field regression version of Mask-RCNN for fetal-head delineation in ultrasound images

2021 ◽  
Author(s):  
Sara Moccia ◽  
Maria Chiara Fiorentino ◽  
Emanuele Frontoni
Keyword(s):  
Fetal Growth ◽  
A Priori ◽  
Absolute Difference ◽  
Ultrasound Images ◽  
Fetal Head ◽  
Mean Absolute Difference ◽  
Distance Field ◽  
Feature Pyramid ◽  
End To End ◽  
Ablation Study

Abstract Background and objectives Fetal head-circumference (HC) measurement from ultrasound (US) images provides useful hints for assessing fetal growth. Such measurement is performed manually during the actual clinical practice, posing issues relevant to intra- and inter-clinician variability. This work presents a fully automatic, deep-learning-based approach to HC delineation, which we named Mask-R$$^{2}$$ 2 CNN. It advances our previous work in the field and performs HC distance-field regression in an end-to-end fashion, without requiring a priori HC localization nor any postprocessing for outlier removal. Methods Mask-R$$^{2}$$ 2 CNN follows the Mask-RCNN architecture, with a backbone inspired by feature-pyramid networks, a region-proposal network and the ROI align. The Mask-RCNN segmentation head is here modified to regress the HC distance field. Results Mask-R$$^{2}$$ 2 CNN was tested on the HC18 Challenge dataset, which consists of 999 training and 335 testing images. With a comprehensive ablation study, we showed that Mask-R$$^{2}$$ 2 CNN achieved a mean absolute difference of 1.95 mm (standard deviation $$=\pm 1.92$$ = ± 1.92  mm), outperforming other approaches in the literature. Conclusions With this work, we proposed an end-to-end model for HC distance-field regression. With our experimental results, we showed that Mask-R$$^{2}$$ 2 CNN may be an effective support for clinicians for assessing fetal growth.

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