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We develop a novel deep contour detection algorithm with a top-down fullyconvolutional encoder-decoder network. Our proposed method, named TD-CEDN,solves two important issues in this low-level vision problem: (1) learningmulti-scale and multi-level features; and (2) applying an effective top-downrefined approach in the networks. TD-CEDN performs the pixel-wise prediction bymeans of leveraging features at all layers of the net. Unlike skip connectionsand previous encoder-decoder methods, we first learn a coarse feature map afterthe encoder stage in a feedforward pass, and then refine this feature map in atop-down strategy during the decoder stage utilizing features at successivelylower layers. Therefore, the deconvolutional process is conducted stepwise,which is guided by Deeply-Supervision Net providing the integrated directsupervision. The above proposed technologies lead to a more precise and clearerprediction. Our proposed algorithm achieved the state-of-the-art on the BSDS500dataset (ODS F-score of 0.788), the PASCAL VOC2012 dataset (ODS F-score of0.588), and and the NYU Depth dataset (ODS F-score of 0.735).

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