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.
Type de document :
Communication dans un congrès
Advances in Neural Information Processing Systems, Dec 2017, Long Beach, United States
Liste complète des métadonnées

Littérature citée [45 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01646112
Contributeur : Xavier Alameda-Pineda <>
Soumis le : jeudi 23 novembre 2017 - 11:43:59
Dernière modification le : mercredi 11 avril 2018 - 01:57:54

Fichier

Xu-NIPS-2017.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01646112, version 1

Collections

Citation

Dan Xu, Wanli Ouyang, Xavier Alameda-Pineda, Elisa Ricci, Xiaogang Wang, et al.. Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction. Advances in Neural Information Processing Systems, Dec 2017, Long Beach, United States. 〈hal-01646112〉

Partager

Métriques

Consultations de la notice

141

Téléchargements de fichiers

56