Performing Weakly Supervised Retail Instance Segmentation via Region Normalization

Instance segmentation can clearly distinguish object instances from the pixel level, which is the foundation for applications in the retail industry. Supervised instance segmentation methods require pixel-level annotations for learning accurate object patterns, which is expensive and labor-intensive to obtain. In the case of retail industry, objects of interest vary frequently and only category-level labels are available, which motivates us to study the weakly supervised instance segmentation algorithm for the retail industry. Although weakly supervised instance segmentation algorithms have been extensively studied on standard benchmarks, e.g., VOC and COCO, directly employing these algorithms to the retail industry usually generates insufficient segmentation accuracy. We found a fundamental reason lies in the specific characteristics of the retail industry. Specifically, one challenge for the traditional benchmark dataset is dramatic appearance variations, but the retail industry only tackles limited object categories and each object possesses fixed appearances. To thoroughly explore characteristics of the retail industry, we propose the region normalization mechanism to thoroughly explore characteristics of the retail industry. It divides an image into regions and requires pixels from the same region to possess consistent features. The region normalization mechanism is a novel attempt to perform normalization within irregular regions, distinct from the existing normalization mechanism. Besides, as region division can influence the effectiveness of the region normalization operation, we explore feature information to dynamically divide an image into regions, under constraints that pixels belonging to the same region should show similar features and keep spatially adjacent. The proposed region normalization mechanism and region division strategy endow a simple but effective solution to weakly supervised instance segmentation for the retail industry. On a large-scale benchmark dataset MVTec D2S, the proposed method performs favorably over existing well-performed methods, and the effectiveness of the region normalization mechanism is demonstrated as well. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xichun Bi [verfasserIn] Lifang Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Retail industry instance segmentation weakly-supervised learning region normalization

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 9(2021), Seite 67761-67775
Übergeordnetes Werk:	volume:9 ; year:2021 ; pages:67761-67775

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2021.3077031

Katalog-ID:	DOAJ057852502

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spelling	10.1109/ACCESS.2021.3077031 doi (DE-627)DOAJ057852502 (DE-599)DOAJ855547b7ef9e4e4aa2a4852566839529 DE-627 ger DE-627 rakwb eng TK1-9971 Xichun Bi verfasserin aut Performing Weakly Supervised Retail Instance Segmentation via Region Normalization 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Instance segmentation can clearly distinguish object instances from the pixel level, which is the foundation for applications in the retail industry. Supervised instance segmentation methods require pixel-level annotations for learning accurate object patterns, which is expensive and labor-intensive to obtain. In the case of retail industry, objects of interest vary frequently and only category-level labels are available, which motivates us to study the weakly supervised instance segmentation algorithm for the retail industry. Although weakly supervised instance segmentation algorithms have been extensively studied on standard benchmarks, e.g., VOC and COCO, directly employing these algorithms to the retail industry usually generates insufficient segmentation accuracy. We found a fundamental reason lies in the specific characteristics of the retail industry. Specifically, one challenge for the traditional benchmark dataset is dramatic appearance variations, but the retail industry only tackles limited object categories and each object possesses fixed appearances. To thoroughly explore characteristics of the retail industry, we propose the region normalization mechanism to thoroughly explore characteristics of the retail industry. It divides an image into regions and requires pixels from the same region to possess consistent features. The region normalization mechanism is a novel attempt to perform normalization within irregular regions, distinct from the existing normalization mechanism. Besides, as region division can influence the effectiveness of the region normalization operation, we explore feature information to dynamically divide an image into regions, under constraints that pixels belonging to the same region should show similar features and keep spatially adjacent. The proposed region normalization mechanism and region division strategy endow a simple but effective solution to weakly supervised instance segmentation for the retail industry. On a large-scale benchmark dataset MVTec D2S, the proposed method performs favorably over existing well-performed methods, and the effectiveness of the region normalization mechanism is demonstrated as well. Retail industry instance segmentation weakly-supervised learning region normalization Electrical engineering. Electronics. Nuclear engineering Lifang Wang verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 67761-67775 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:67761-67775 https://doi.org/10.1109/ACCESS.2021.3077031 kostenfrei https://doaj.org/article/855547b7ef9e4e4aa2a4852566839529 kostenfrei https://ieeexplore.ieee.org/document/9420753/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 67761-67775
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callnumber-first	T - Technology
author	Xichun Bi
spellingShingle	Xichun Bi misc TK1-9971 misc Retail industry misc instance segmentation misc weakly-supervised learning misc region normalization misc Electrical engineering. Electronics. Nuclear engineering Performing Weakly Supervised Retail Instance Segmentation via Region Normalization
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topic_title	TK1-9971 Performing Weakly Supervised Retail Instance Segmentation via Region Normalization Retail industry instance segmentation weakly-supervised learning region normalization
topic	misc TK1-9971 misc Retail industry misc instance segmentation misc weakly-supervised learning misc region normalization misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Retail industry misc instance segmentation misc weakly-supervised learning misc region normalization misc Electrical engineering. Electronics. Nuclear engineering
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title	Performing Weakly Supervised Retail Instance Segmentation via Region Normalization
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title_full	Performing Weakly Supervised Retail Instance Segmentation via Region Normalization
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title_sort	performing weakly supervised retail instance segmentation via region normalization
callnumber	TK1-9971
title_auth	Performing Weakly Supervised Retail Instance Segmentation via Region Normalization
abstract	Instance segmentation can clearly distinguish object instances from the pixel level, which is the foundation for applications in the retail industry. Supervised instance segmentation methods require pixel-level annotations for learning accurate object patterns, which is expensive and labor-intensive to obtain. In the case of retail industry, objects of interest vary frequently and only category-level labels are available, which motivates us to study the weakly supervised instance segmentation algorithm for the retail industry. Although weakly supervised instance segmentation algorithms have been extensively studied on standard benchmarks, e.g., VOC and COCO, directly employing these algorithms to the retail industry usually generates insufficient segmentation accuracy. We found a fundamental reason lies in the specific characteristics of the retail industry. Specifically, one challenge for the traditional benchmark dataset is dramatic appearance variations, but the retail industry only tackles limited object categories and each object possesses fixed appearances. To thoroughly explore characteristics of the retail industry, we propose the region normalization mechanism to thoroughly explore characteristics of the retail industry. It divides an image into regions and requires pixels from the same region to possess consistent features. The region normalization mechanism is a novel attempt to perform normalization within irregular regions, distinct from the existing normalization mechanism. Besides, as region division can influence the effectiveness of the region normalization operation, we explore feature information to dynamically divide an image into regions, under constraints that pixels belonging to the same region should show similar features and keep spatially adjacent. The proposed region normalization mechanism and region division strategy endow a simple but effective solution to weakly supervised instance segmentation for the retail industry. On a large-scale benchmark dataset MVTec D2S, the proposed method performs favorably over existing well-performed methods, and the effectiveness of the region normalization mechanism is demonstrated as well.
abstractGer	Instance segmentation can clearly distinguish object instances from the pixel level, which is the foundation for applications in the retail industry. Supervised instance segmentation methods require pixel-level annotations for learning accurate object patterns, which is expensive and labor-intensive to obtain. In the case of retail industry, objects of interest vary frequently and only category-level labels are available, which motivates us to study the weakly supervised instance segmentation algorithm for the retail industry. Although weakly supervised instance segmentation algorithms have been extensively studied on standard benchmarks, e.g., VOC and COCO, directly employing these algorithms to the retail industry usually generates insufficient segmentation accuracy. We found a fundamental reason lies in the specific characteristics of the retail industry. Specifically, one challenge for the traditional benchmark dataset is dramatic appearance variations, but the retail industry only tackles limited object categories and each object possesses fixed appearances. To thoroughly explore characteristics of the retail industry, we propose the region normalization mechanism to thoroughly explore characteristics of the retail industry. It divides an image into regions and requires pixels from the same region to possess consistent features. The region normalization mechanism is a novel attempt to perform normalization within irregular regions, distinct from the existing normalization mechanism. Besides, as region division can influence the effectiveness of the region normalization operation, we explore feature information to dynamically divide an image into regions, under constraints that pixels belonging to the same region should show similar features and keep spatially adjacent. The proposed region normalization mechanism and region division strategy endow a simple but effective solution to weakly supervised instance segmentation for the retail industry. On a large-scale benchmark dataset MVTec D2S, the proposed method performs favorably over existing well-performed methods, and the effectiveness of the region normalization mechanism is demonstrated as well.
abstract_unstemmed	Instance segmentation can clearly distinguish object instances from the pixel level, which is the foundation for applications in the retail industry. Supervised instance segmentation methods require pixel-level annotations for learning accurate object patterns, which is expensive and labor-intensive to obtain. In the case of retail industry, objects of interest vary frequently and only category-level labels are available, which motivates us to study the weakly supervised instance segmentation algorithm for the retail industry. Although weakly supervised instance segmentation algorithms have been extensively studied on standard benchmarks, e.g., VOC and COCO, directly employing these algorithms to the retail industry usually generates insufficient segmentation accuracy. We found a fundamental reason lies in the specific characteristics of the retail industry. Specifically, one challenge for the traditional benchmark dataset is dramatic appearance variations, but the retail industry only tackles limited object categories and each object possesses fixed appearances. To thoroughly explore characteristics of the retail industry, we propose the region normalization mechanism to thoroughly explore characteristics of the retail industry. It divides an image into regions and requires pixels from the same region to possess consistent features. The region normalization mechanism is a novel attempt to perform normalization within irregular regions, distinct from the existing normalization mechanism. Besides, as region division can influence the effectiveness of the region normalization operation, we explore feature information to dynamically divide an image into regions, under constraints that pixels belonging to the same region should show similar features and keep spatially adjacent. The proposed region normalization mechanism and region division strategy endow a simple but effective solution to weakly supervised instance segmentation for the retail industry. On a large-scale benchmark dataset MVTec D2S, the proposed method performs favorably over existing well-performed methods, and the effectiveness of the region normalization mechanism is demonstrated as well.
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title_short	Performing Weakly Supervised Retail Instance Segmentation via Region Normalization
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