Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction
Abstract
Recent works have shown that exploiting multi-scale representations deeply learned via convolutional neural networks (CNN) is of tremendous importance for accurate contour detection. This paper presents a novel approach for predicting contours which advances the state of the art in two fundamental aspects, i.e. multi-scale feature generation and fusion. Different from previous works directly consider- ing multi-scale feature maps obtained from the inner layers of a primary CNN architecture, we introduce a hierarchical deep model which produces more rich and complementary representations. Furthermore, to refine and robustly fuse the representations learned at different scales, the novel Attention-Gated Conditional Random Fields (AG-CRFs) are proposed. The experiments ran on two publicly available datasets (BSDS500 and NYUDv2) demonstrate the effectiveness of the latent AG-CRF model and of the overall hierarchical framework.
Keywords
Cite
@article{arxiv.1801.00524,
title = {Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction},
author = {Dan Xu and Wanli Ouyang and Xavier Alameda-Pineda and Elisa Ricci and Xiaogang Wang and Nicu Sebe},
journal= {arXiv preprint arXiv:1801.00524},
year = {2018}
}
Comments
Accepted at NIPS 2017