Transcription of Rethinking Semantic Segmentation From a Sequence-to ...
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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.
More recently, a pure vision transformer or ViT [15] has shown to be effective for image classification tasks. It thus provides direct evidence that the traditional stacked convo-lutionlayer(i.e., CNN)designcanbechallengedandimage features do not necessarily need to be learned progressively from local to global context by reducing spatial ...
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