A transformer-based semantic segmentation model for street fashion images

Dingjie Peng; Wataru Kameyama

doi:10.1117/12.2666583

25 March 2023 A transformer-based semantic segmentation model for street fashion images

Dingjie Peng, Wataru Kameyama

Proceedings Volume 12592, International Workshop on Advanced Imaging Technology (IWAIT) 2023; 125920W (2023) https://doi.org/10.1117/12.2666583
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2023, 2023, Jeju, Korea, Republic of

Abstract

Semantic segmentation is a pixel-level classification problem in computer vision, in which pixels of the same class are grouped into a single category in order to interpret pictures at the pixel level. In this field, semantic segmentation of street fashion images is a challenging task since the clothing items would appear with wide variations in fabrics, layering, occlusion and viewpoint. To help better understanding the street fashion images, we propose a lightweight Semantic Context Aware Transformer (SCAT) to be applied to the semantic segmentation task for street fashion images, which integrates semantic context into the encoding, and models the relationship between multi-level outputs from transformer layers. Extensive experiments and comparisons show that the proposal achieves the state-of-the-art results on ModaNet dataset with relatively small model size, with over 1.1 point improvement compared to Shunted Transformer, and even surpasses other CNNs and Transformers with a large margin of over 2 point in mIoU.

Citation Download Citation

Dingjie Peng and Wataru Kameyama "A transformer-based semantic segmentation model for street fashion images", Proc. SPIE 12592, International Workshop on Advanced Imaging Technology (IWAIT) 2023, 125920W (25 March 2023); https://doi.org/10.1117/12.2666583

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