MPAR-RCNN: a multi-task network for multiple person detection with attribute recognition
Multi-label attribute recognition is a critical task in computer vision, with applications ranging across diverse fields. This problem often involves detecting objects with multiple attributes, necessitating sophisticated models capable of both high-level differentiation and fine-grained feature ext...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1454488/full |
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| author | S. Raghavendra S. K. Abhilash Venu Madhav Nookala Jayashree Shetty Praveen Gurunath Bharathi |
| author_facet | S. Raghavendra S. K. Abhilash Venu Madhav Nookala Jayashree Shetty Praveen Gurunath Bharathi |
| author_sort | S. Raghavendra |
| collection | DOAJ |
| description | Multi-label attribute recognition is a critical task in computer vision, with applications ranging across diverse fields. This problem often involves detecting objects with multiple attributes, necessitating sophisticated models capable of both high-level differentiation and fine-grained feature extraction. The integration of object detection and attribute recognition typically relies on approaches such as dual-stage networks, where accurate predictions depend on advanced feature extraction techniques, such as Region of Interest (RoI) pooling. To meet these demands, an efficient method that achieves both reliable detection and attribute classification in a unified framework is essential. This study introduces an innovative MTL framework designed to incorporate Multi-Person Attribute Recognition (MPAR) within a single-model architecture. Named MPAR-RCNN, this framework unifies object detection and attribute recognition tasks through a spatially aware, shared backbone, facilitating efficient and accurate multi-label prediction. Unlike the traditional Fast Region-based Convolutional Neural Network (R-CNN), which separately manages person detection and attribute classification with a dual-stage network, the MPAR-RCNN architecture optimizes both tasks within a single structure. Validated on the WIDER (Web Image Dataset for Event Recognition) dataset, the proposed model demonstrates an improvement over current state-of-the-art (SOTA) architectures, showcasing its potential in advancing multi-label attribute recognition. |
| format | Article |
| id | doaj-art-77d593a4310f427599239968b5052400 |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-77d593a4310f427599239968b50524002025-02-07T06:49:49ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-02-01810.3389/frai.2025.14544881454488MPAR-RCNN: a multi-task network for multiple person detection with attribute recognitionS. Raghavendra0S. K. Abhilash1Venu Madhav Nookala2Jayashree Shetty3Praveen Gurunath Bharathi4Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaKPIT Technologies, Bengaluru, IndiaKPIT Technologies, Bengaluru, IndiaDepartment of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaNuclear Medicine and Molecular Imaging, Department of Radiology, Stanford Medicine, Palo Alto, CA, United StatesMulti-label attribute recognition is a critical task in computer vision, with applications ranging across diverse fields. This problem often involves detecting objects with multiple attributes, necessitating sophisticated models capable of both high-level differentiation and fine-grained feature extraction. The integration of object detection and attribute recognition typically relies on approaches such as dual-stage networks, where accurate predictions depend on advanced feature extraction techniques, such as Region of Interest (RoI) pooling. To meet these demands, an efficient method that achieves both reliable detection and attribute classification in a unified framework is essential. This study introduces an innovative MTL framework designed to incorporate Multi-Person Attribute Recognition (MPAR) within a single-model architecture. Named MPAR-RCNN, this framework unifies object detection and attribute recognition tasks through a spatially aware, shared backbone, facilitating efficient and accurate multi-label prediction. Unlike the traditional Fast Region-based Convolutional Neural Network (R-CNN), which separately manages person detection and attribute classification with a dual-stage network, the MPAR-RCNN architecture optimizes both tasks within a single structure. Validated on the WIDER (Web Image Dataset for Event Recognition) dataset, the proposed model demonstrates an improvement over current state-of-the-art (SOTA) architectures, showcasing its potential in advancing multi-label attribute recognition.https://www.frontiersin.org/articles/10.3389/frai.2025.1454488/fullattribute recognitionconvolution neural networkhuman attribute recognitionmulti-task learningobject detection |
| spellingShingle | S. Raghavendra S. K. Abhilash Venu Madhav Nookala Jayashree Shetty Praveen Gurunath Bharathi MPAR-RCNN: a multi-task network for multiple person detection with attribute recognition Frontiers in Artificial Intelligence attribute recognition convolution neural network human attribute recognition multi-task learning object detection |
| title | MPAR-RCNN: a multi-task network for multiple person detection with attribute recognition |
| title_full | MPAR-RCNN: a multi-task network for multiple person detection with attribute recognition |
| title_fullStr | MPAR-RCNN: a multi-task network for multiple person detection with attribute recognition |
| title_full_unstemmed | MPAR-RCNN: a multi-task network for multiple person detection with attribute recognition |
| title_short | MPAR-RCNN: a multi-task network for multiple person detection with attribute recognition |
| title_sort | mpar rcnn a multi task network for multiple person detection with attribute recognition |
| topic | attribute recognition convolution neural network human attribute recognition multi-task learning object detection |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1454488/full |
| work_keys_str_mv | AT sraghavendra mparrcnnamultitasknetworkformultiplepersondetectionwithattributerecognition AT skabhilash mparrcnnamultitasknetworkformultiplepersondetectionwithattributerecognition AT venumadhavnookala mparrcnnamultitasknetworkformultiplepersondetectionwithattributerecognition AT jayashreeshetty mparrcnnamultitasknetworkformultiplepersondetectionwithattributerecognition AT praveengurunathbharathi mparrcnnamultitasknetworkformultiplepersondetectionwithattributerecognition |