UperFormer: A Multi-scale Transformer-based Decoder for Semantic Segmentation

• URL: http://arxiv.org/abs/2211.13928v1
• Date: Fri, 25 Nov 2022 06:51:07 GMT
• Title: UperFormer: A Multi-scale Transformer-based Decoder for Semantic Segmentation
• Authors: Jing Xu, Wentao Shi, Pan Gao, Zhengwei Wang, Qizhu Li
• Abstract summary: We propose a novel transformer-based decoder called UperFormer. UperFormer is plug-and-play for hierarchical encoders and attains high quality segmentation results regardless of encoder architecture. Our best model yields a single-scale mIoU of 50.18, and a multi-scale mIoU of 51.8, which is on-par with the current state-of-art model.
• Score: 12.712880544703332
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While a large number of recent works on semantic segmentation focus on designing and incorporating a transformer-based encoder, much less attention and vigor have been devoted to transformer-based decoders. For such a task whose hallmark quest is pixel-accurate prediction, we argue that the decoder stage is just as crucial as that of the encoder in achieving superior segmentation performance, by disentangling and refining the high-level cues and working out object boundaries with pixel-level precision. In this paper, we propose a novel transformer-based decoder called UperFormer, which is plug-and-play for hierarchical encoders and attains high quality segmentation results regardless of encoder architecture. UperFormer is equipped with carefully designed multi-head skip attention units and novel upsampling operations. Multi-head skip attention is able to fuse multi-scale features from backbones with those in decoders. The upsampling operation, which incorporates feature from encoder, can be more friendly for object localization. It brings a 0.4% to 3.2% increase compared with traditional upsampling methods. By combining UperFormer with Swin Transformer (Swin-T), a fully transformer-based symmetric network is formed for semantic segmentation tasks. Extensive experiments show that our proposed approach is highly effective and computationally efficient. On Cityscapes dataset, we achieve state-of-the-art performance. On the more challenging ADE20K dataset, our best model yields a single-scale mIoU of 50.18, and a multi-scale mIoU of 51.8, which is on-par with the current state-of-art model, while we drastically cut the number of FLOPs by 53.5%. Our source code and models are publicly available at: https://github.com/shiwt03/UperFormer

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