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Rethinking Semantic Segmentation From a Sequence-to ...
Rethinking Semantic Segmentation from a Sequence-to -Sequence Perspectivewith TransformersSixiao Zheng1*Jiachen Lu1 Hengshuang Zhao2 Xiatian Zhu3 Zekun Luo4 Yabiao Wang4 Yanwei Fu1 Jianfeng Feng1 Tao Xiang3, 5 Philip Torr2Li Zhang1 1 Fudan University2 University of Oxford3 University of Surrey4 Tencent Youtu Lab5 Facebook recent Semantic Segmentation methods adopta fully-convolutional network (FCN) with an encoder-decoder architecture. The encoder progressively reducesthe spatial resolution and learns more abstract/semanticvisual concepts with larger receptive fields. Since contextmodeling is critical for Segmentation , the latest efforts havebeen focused on increasing the receptive field, through ei-ther dilated/atrous convolutions or inserting attention mod-ules. However, the encoder-decoder based FCN architec-ture remains unchanged. In this paper, we aim to providean alternative perspective by treating Semantic segmenta-tion as a Sequence-to -sequence prediction task.
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective ... visual concepts with larger receptive fields. Since context modeling is critical for segmentation, the latest efforts have ... features do not necessarily need to be learned progressively from local to global context by reducing spatial resolution.
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