Name
Title | ||
|---|---|---|
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 considering 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. |
Year | Venue | DocType |
|---|---|---|
2017 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017) | Conference |
Volume | ISSN | Citations |
30 | 1049-5258 | 6 |
PageRank | References | Authors |
0.40 | 31 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dan Xu | 1 | 342 | 16.39 |
| Wanli Ouyang | 2 | 2371 | 105.17 |
| Xavier Alameda-Pineda | 3 | 232 | 28.24 |
| Elisa Ricci 0002 | 4 | 1393 | 73.75 |
| Xiaogang Wang | 5 | 9647 | 386.70 |
| Sebe, Nicu | 6 | 6 | 0.40 |