MetaSeg: MetaFormer-based Global Contexts-aware Network for Efficient Semantic Segmentation
<|reference_start|>MetaSeg: MetaFormer-based Global Contexts-aware Network for Efficient Semantic Segmentation: Beyond the Transformer, it is important to explore how to exploit the capacity of the MetaFormer, an architecture that is fundamental to the performance improvements of the Transformer. Previous studies have exploited it only for the backbone network. Unlike previous studies, we explore the capacity of the Metaformer architecture more extensively in the semantic segmentation task. We propose a powerful semantic segmentation network, MetaSeg, which leverages the Metaformer architecture from the backbone to the decoder. Our MetaSeg shows that the MetaFormer architecture plays a significant role in capturing the useful contexts for the decoder as well as for the backbone. In addition, recent segmentation methods have shown that using a CNN-based backbone for extracting the spatial information and a decoder for extracting the global information is more effective than using a transformer-based backbone with a CNN-based decoder. This motivates us to adopt the CNN-based backbone using the MetaFormer block and design our MetaFormer-based decoder, which consists of a novel self-attention module to capture the global contexts. To consider both the global contexts extraction and the computational efficiency of the self-attention for semantic segmentation, we propose a Channel Reduction Attention (CRA) module that reduces the channel dimension of the query and key into the one dimension. In this way, our proposed MetaSeg outperforms the previous state-of-the-art methods with more efficient computational costs on popular semantic segmentation and a medical image segmentation benchmark, including ADE20K, Cityscapes, COCO-stuff, and Synapse. The code is available at https://github.com/hyunwoo137/MetaSeg.<|reference_end|>
@article{kang2024metaseg:,
    title={MetaSeg: MetaFormer-based Global Contexts-aware Network for Efficient
  Semantic Segmentation},
    author={Beoungwoo Kang, Seunghun Moon, Yubin Cho, Hyunwoo Yu, Suk-Ju Kang},
    journal={arXiv preprint arXiv:2408.07576},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07576},
    primaryClass={cs.CV cs.AI}
}