Occlusion-aware spatial attention transformer for occluded object recognition

Park, Jihun

doi:10.1016/j.patrec.2022.05.006

Cited by 15 publications

(2 citation statements)

References 19 publications

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“…Gajbhiye et al [34] combined the channel attention mechanism and convolutional neural network to generate letters for remote sensing images and conducted model evaluation in the test set, obtaining good results. Additionally, many scholars have integrated the spatial attention module into the deep learning network model to improve the training effect of the model [35][36][37]. Some geological experts focus on the spatial distribution of geological images and data and achieve good results in image modeling and image classification.…”

Section: The Mechanism Of Attention Used In Geodata Analysismentioning

confidence: 99%

Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning

Gao,

Wang,

Zhang

et al. 2023

Sustainability

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In recent years, the intelligent identification and prediction of ore deposits based on deep learning algorithm and image processing technology has gradually become one of the main research frontiers in the field of geological and metallogenic prediction. However, this method also has many problems that need to be solved. For example: (1) There are very few trainable image samples containing mineral point labels; (2) the geological image features are small and irregular, and the image similarity is high; (3) it is difficult to calculate the influence of different geological prospecting factors on ore mineralization. Based on this, this paper constructs a deep learning network model multiscale feature attention framework (MFAF) based on geoimage data. The results show that the MFCA-Net module in the MFAF model can solve the problem of scarce mine label images to a certain extent. In addition, the channel attention mechanism SE-Net module can quantify the difference in influence of different source factors on mineralization. The prediction map is obtained by applying the MFAF model in the study of deposit identification and prediction in the research area of the southern section of the Qin-hang metallogenic belt. The experimental results show that the areas numbered 5, 9, 16, 28, 34, 41, 50, 72, 74, 75, 80, 97, 101, 124, and 130 have great metallogenic potential and this method would be a promising tool for metallogenic prediction. A large number of experimental results show that this method has obvious advantages over other state-of-the-art methods in the prediction of prospecting target areas, and the prediction effect in the samples with mines is greatly improved. The multi-scale feature fusion and attention mechanism MFAF in this paper can provide a new way of thinking for geologists in mineral exploration. The research of this paper also provides resource guarantees and technical support for the sustainable exploitation of mineral resources and the sustainable growth of society and economy.

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Section: The Mechanism Of Attention Used In Geodata Analysismentioning

confidence: 99%

Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning

Gao,

Wang,

Zhang

et al. 2023

Sustainability

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“…Ma et al [ 17 ] proposed a robust face recognition approach based on a sparse network with limited probability, built a sparse image network with limited probability, and acquired the overall training images from a global perspective for recognition. Heo et al [ 18 ] proposed an occlusion-aware spatial attention transformer (OSAT) architecture based on a visual transformer (ViT), CutMix strengthening, and occlusion mask predictor (OMP) to solve the occlusion problem. Xu et al [ 19 ] proposed a double-active-layer-based CNN to recognize facial expressions with high accuracy by learning robust and discriminative features from data.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Occluded Goods under Prior Inference Based on Generative Adversarial Network

Cao

Xie

Liu

et al. 2023

Sensors

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Aiming at the recognition of intelligent retail dynamic visual container goods, two problems that lead to low recognition accuracy must be addressed; one is the lack of goods features caused by the occlusion of the hand, and the other is the high similarity of goods. Therefore, this study proposes an approach for occluding goods recognition based on a generative adversarial network combined with prior inference to address the two abovementioned problems. With DarkNet53 as the backbone network, semantic segmentation is used to locate the occluded part in the feature extraction network, and simultaneously, the YOLOX decoupling head is used to obtain the detection frame. Subsequently, a generative adversarial network under prior inference is used to restore and expand the features of the occluded parts, and a multi-scale spatial attention and effective channel attention weighted attention mechanism module is proposed to select fine-grained features of goods. Finally, a metric learning method based on von Mises–Fisher distribution is proposed to increase the class spacing of features to achieve the effect of feature distinction, whilst the distinguished features are utilized to recognize goods at a fine-grained level. The experimental data used in this study were all obtained from the self-made smart retail container dataset, which contains a total of 12 types of goods used for recognition and includes four couples of similar goods. Experimental results reveal that the peak signal-to-noise ratio and structural similarity under improved prior inference are 0.7743 and 0.0183 higher than those of the other models, respectively. Compared with other optimal models, mAP improves the recognition accuracy by 1.2% and the recognition accuracy by 2.82%. This study solves two problems: one is the occlusion caused by hands, and the other is the high similarity of goods, thus meeting the requirements of commodity recognition accuracy in the field of intelligent retail and exhibiting good application prospects.

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TinyConv-PVT: A Deeper Fusion Model of CNN and Transformer for Tiny Dataset

Zhang,

2024

Lecture Notes in Computer Science

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Occlusion-aware spatial attention transformer for occluded object recognition

Cited by 15 publications

References 19 publications

Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning

Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning

Recognition of Occluded Goods under Prior Inference Based on Generative Adversarial Network

TinyConv-PVT: A Deeper Fusion Model of CNN and Transformer for Tiny Dataset

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