Learning to Refine Object Contours with a Top-Down Fully Convolutional Encoder-Decoder Network

Yahui Liu; Jian Yao; Li Li; Xiaohu Lu; Jing Han

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arXiv · 1705.04456

Learning to Refine Object Contours with a Top-Down Fully Convolutional Encoder-Decoder Network

Yahui Liu·Jian Yao·Li Li·Xiaohu Lu·Jing Han

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Abstract

We develop a novel deep contour detection algorithm with a top-down fully convolutional encoder-decoder network. Our proposed method, named TD-CEDN, solves two important issues in this low-level vision problem: (1) learning multi-scale and multi-level features; and (2) applying an effective top-down refined approach in the networks. TD-CEDN performs the pixel-wise prediction by means of leveraging features at all layers of the net. Unlike skip connections and previous encoder-decoder methods, we first learn a coarse feature map after the encoder stage in a feedforward pass, and then refine this feature map in a top-down strategy during the decoder stage utilizing features at successively lower layers. Therefore, the deconvolutional process is conducted stepwise, which is guided by Deeply-Supervision Net providing the integrated direct supervision. The above proposed technologies lead to a more precise and clearer prediction. Our proposed algorithm achieved the state-of-the-art on the BSDS500 dataset (ODS F-score of 0.788), the PASCAL VOC2012 dataset (ODS F-score of 0.588), and and the NYU Depth dataset (ODS F-score of 0.735).

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BibTeX RIS

Yahui Liu, Jian Yao, Li Li, Xiaohu Lu, Jing Han. 2017-05-12. Learning to Refine Object Contours with a Top-Down Fully Convolutional Encoder-Decoder Network. https://arxiv.org/abs/1705.04456

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