ViT-PGC: vision transformer for pedestrian gender classification on small-size dataset
Pedestrian gender classification (PGC) is a key task in full-body-based pedestrian image analysis and has become an important area in applications like content-based image retrieval, visual surveillance, smart city, and demographic collection. In the last decade, convolutional neural networks (CNN)...
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| Published in | Pattern analysis and applications : PAA Vol. 26; no. 4; pp. 1805 - 1819 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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London
Springer London
01.11.2023
Springer Nature B.V |
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| Online Access | Get full text |
| ISSN | 1433-7541 1433-755X |
| DOI | 10.1007/s10044-023-01196-2 |
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| Abstract | Pedestrian gender classification (PGC) is a key task in full-body-based pedestrian image analysis and has become an important area in applications like content-based image retrieval, visual surveillance, smart city, and demographic collection. In the last decade, convolutional neural networks (CNN) have appeared with great potential and with reliable choices for vision tasks, such as object classification, recognition, detection, etc. But CNN has a limited local receptive field that prevents them from learning information about the global context. In contrast, a vision transformer (ViT) is a better alternative to CNN because it utilizes a self-attention mechanism to attend to a different patch of an input image. In this work, generic and effective modules such as locality self-attention (LSA), and shifted patch tokenization (SPT)-based vision transformer model are explored for the PGC task. With the use of these modules in ViT, it is successfully able to learn from stretch even on small-size (SS) datasets and overcome the lack of locality inductive bias. Through extensive experimentation, we found that the proposed ViT model produced better results in terms of overall and mean accuracies. The better results confirm that ViT outperformed state-of-the-art (SOTA) PGC methods. |
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| AbstractList | Pedestrian gender classification (PGC) is a key task in full-body-based pedestrian image analysis and has become an important area in applications like content-based image retrieval, visual surveillance, smart city, and demographic collection. In the last decade, convolutional neural networks (CNN) have appeared with great potential and with reliable choices for vision tasks, such as object classification, recognition, detection, etc. But CNN has a limited local receptive field that prevents them from learning information about the global context. In contrast, a vision transformer (ViT) is a better alternative to CNN because it utilizes a self-attention mechanism to attend to a different patch of an input image. In this work, generic and effective modules such as locality self-attention (LSA), and shifted patch tokenization (SPT)-based vision transformer model are explored for the PGC task. With the use of these modules in ViT, it is successfully able to learn from stretch even on small-size (SS) datasets and overcome the lack of locality inductive bias. Through extensive experimentation, we found that the proposed ViT model produced better results in terms of overall and mean accuracies. The better results confirm that ViT outperformed state-of-the-art (SOTA) PGC methods. |
| Author | Yasmin, Mussarat Asim, Usman Fayyaz, Muhammad Abbas, Farhat |
| Author_xml | – sequence: 1 givenname: Farhat orcidid: 0000-0001-6182-3244 surname: Abbas fullname: Abbas, Farhat email: farhatabbas421@gmail.com organization: Department of Computer Science, COMSATS University Islamabad – sequence: 2 givenname: Mussarat surname: Yasmin fullname: Yasmin, Mussarat organization: Department of Computer Science, COMSATS University Islamabad – sequence: 3 givenname: Muhammad surname: Fayyaz fullname: Fayyaz, Muhammad organization: Department of Computer Science, FAST-National University of Computer and Emerging Sciences (NUCES) – sequence: 4 givenname: Usman surname: Asim fullname: Asim, Usman organization: DeltaX |
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| Keywords | Vision transformer Pedestrian gender classification Deep CNN models LSA and SPT SS datasets |
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| Snippet | Pedestrian gender classification (PGC) is a key task in full-body-based pedestrian image analysis and has become an important area in applications like... |
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| Title | ViT-PGC: vision transformer for pedestrian gender classification on small-size dataset |
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