Fashion image analysis using single-stage detector

Hyojin Kim; Doohee Lee; Chanyong Kim; Asif Aziz Memon; Asim Niaz; Seohyun Lee; Soyeon Yang; Francesco Piccialli; Kwang Nam Choi

doi:10.1117/12.2579990

10 November 2020 Fashion image analysis using single-stage detector

Hyojin Kim, Doohee Lee, Chanyong Kim, Asif Aziz Memon, Asim Niaz, Seohyun Lee, Soyeon Yang, Francesco Piccialli, Kwang Nam Choi

Author Affiliations +

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 1158418 (2020) https://doi.org/10.1117/12.2579990
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

Abstract

Clothing detection and landmark detection are important techniques in fashion image analysis. The availability of large annotated fashion datasets has made fashion image analysis a hot research topic. This paper proposes a single-stage detector that performs bounding box detection, fashion landmark detection, and can also predict end-to-end clothing category classification. This parallel processing provides improved time efficiency than the later technique that performs regional proposals first and then prediction module. The proposed network is designed with the revision of the EfficientDet model announced by Google Brain. The proposed approach can also be used within a real application because it can operate efficiently and quickly from the inference latency perspective.

Citation Download Citation

Hyojin Kim, Doohee Lee, Chanyong Kim, Asif Aziz Memon, Asim Niaz, Seohyun Lee, Soyeon Yang, Francesco Piccialli, and Kwang Nam Choi "Fashion image analysis using single-stage detector", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 1158418 (10 November 2020); https://doi.org/10.1117/12.2579990

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