Pyramid Feature Attention Network for Saliency detection (1903.00179v2)

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.

• The paper introduces a CPFE module that uses atrous convolutions with varied dilation rates to capture diverse, context-aware features.
• The network employs channel-wise and spatial attention to selectively enhance salient regions and refine object boundaries.
• The novel edge preservation loss improves boundary localization, leading to superior performance on benchmarks like DUTS-test and ECSSD.

Pyramid Feature Attention Network for Saliency Detection

Overview

This paper addresses the challenge of saliency detection in computer vision by proposing a novel approach, the Pyramid Feature Attention (PFA) network. Saliency detection focuses on identifying prominent or attention-attracting parts of an image, serving as a crucial step in various applications such as object detection, visual tracking, and image retrieval. The authors present an innovative framework that strategically harnesses multi-scale features to improve detection accuracy and reduce noise.

Key Contributions

1. Context-aware Pyramid Feature Extraction (CPFE): The authors introduce the CPFE module designed to enhance high-level feature maps by capturing diverse context-aware, multi-scale information through atrous convolutions with different dilation rates. This facilitates the extraction of features that are invariant to scale and shape.
2. Attention Mechanisms:
   • Channel-wise Attention (CA): Applied after CPFE, this mechanism selectively enhances crucial channels in high-level features, optimizing relevance for saliency detection.
   • Spatial Attention (SA): Utilized on low-level features, SA refines boundaries by focusing attention on crucial spatial regions, thereby filtering out irrelevant background details.
3. Edge Preservation Loss: A novel loss function is introduced to guide the network in capturing finer boundary details of salient objects, enhancing the precision of the saliency maps.

Results and Implications

The PFA network demonstrates superior performance across multiple benchmark datasets, outperforming contemporary state-of-the-art methods using various evaluation metrics such as weighted F-measure and mean absolute error (MAE).

• On datasets like DUTS-test, ECSSD, and HKU-IS, the PFA network showed marked improvements, highlighting its robustness in handling complex saliency detection scenarios with varied object scales and backgrounds.

The integration of context-aware feature extraction and sophisticated attention mechanisms offers practical enhancement in object boundary delineation and map accuracy, suggesting potential applications in real-time systems where boundary precision is critical.

The inclusion of the edge preservation loss differentiates this approach by improving detailed boundary localization, which could prove beneficial in domains requiring refined segmentation, such as medical imaging and autonomous driving.

Future Directions

The proposed methodology opens up avenues for further exploration in salient object detection, particularly in extending multi-scale attention frameworks and exploring different attention strategies. Additionally, investigating the scalability of this approach with other architectures, such as Transformer-based models, could be a promising direction. Another potential area of exploration is adapting the edge preservation loss to other domains beyond vision, where boundary precision and feature selectivity are critical.

Overall, the Pyramid Feature Attention network presents a refined approach to saliency detection, emphasizing the amalgamation of context-aware, hierarchical feature extraction with nuanced attention mechanisms for superior object detection performance.