Pyramid Feature Attention Network for Saliency detection

Code

• github.com/CaitinZhao/cvpr2019_Pyramid-Feature-Attention-Network-for-Saliency-detection
  OfficialIn papertf★ 0
• github.com/halbielee/EPS
  pytorch★ 89
• github.com/Wu0409/HSC_WSSS
  pytorch★ 19
• github.com/ThorraySJTU/Pytorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection
  pytorch★ 0
• github.com/sairajk/PyTorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection
  pytorch★ 0
• github.com/dizaiyoufang/pytorch_PFAN
  pytorch★ 0

Abstract

Saliency detection is one of the basic challenges in computer vision. How to extract effective features is a critical point for saliency detection. Recent methods mainly adopt integrating multi-scale convolutional features indiscriminately. However, not all features are useful for saliency detection and some even cause interferences. To solve this problem, we propose Pyramid Feature Attention network to focus on effective high-level context features and low-level spatial structural features. First, we design Context-aware Pyramid Feature Extraction (CPFE) module for multi-scale high-level feature maps to capture rich context features. Second, we adopt channel-wise attention (CA) after CPFE feature maps and spatial attention (SA) after low-level feature maps, then fuse outputs of CA & SA together. Finally, we propose an edge preservation loss to guide network to learn more detailed information in boundary localization. Extensive evaluations on five benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art approaches under different evaluation metrics.

Tasks

Benchmark Results

Reproductions