Global Context-Aware Progressive Aggregation Network for Salient Object Detection (2003.00651v1)

Abstract: Deep convolutional neural networks have achieved competitive performance in salient object detection, in which how to learn effective and comprehensive features plays a critical role. Most of the previous works mainly adopted multiple level feature integration yet ignored the gap between different features. Besides, there also exists a dilution process of high-level features as they passed on the top-down pathway. To remedy these issues, we propose a novel network named GCPANet to effectively integrate low-level appearance features, high-level semantic features, and global context features through some progressive context-aware Feature Interweaved Aggregation (FIA) modules and generate the saliency map in a supervised way. Moreover, a Head Attention (HA) module is used to reduce information redundancy and enhance the top layers features by leveraging the spatial and channel-wise attention, and the Self Refinement (SR) module is utilized to further refine and heighten the input features. Furthermore, we design the Global Context Flow (GCF) module to generate the global context information at different stages, which aims to learn the relationship among different salient regions and alleviate the dilution effect of high-level features. Experimental results on six benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively.

• The paper introduces the Feature Interweaved Aggregation (FIA) module that fuses low-level details, high-level semantics, and global context to enhance saliency predictions.
• It presents the Global Context Flow (GCF), Head Attention (HA), and Self Refinement (SR) modules that preserve feature integrity and emphasize prominent regions in images.
• Empirical results show that GCPANet outperforms 12 state-of-the-art methods on six benchmark datasets by achieving top F-measure scores and the lowest Mean Absolute Error.

Global Context-Aware Progressive Aggregation Network for Salient Object Detection

The paper presents a novel deep learning architecture, the Global Context-Aware Progressive Aggregation Network (GCPANet), specifically designed for salient object detection. Salient object detection is a computational approach aimed at identifying the most prominent regions in an image that capture human attention. This task is critical for applications such as image understanding, retrieval, and object tracking.

Key Contributions

1. Feature Interweaved Aggregation (FIA) Module: One of the prominent features of GCPANet is the FIA module, which innovatively interweaves low-level appearance details, high-level semantic content, and global contextual information. This amalgamation suppresses noise and enhances the integrity of the structural information in the saliency predictions.
2. Global Context Flow (GCF) Module: The introduction of the GCF module is a mechanism to alleviate the dilution of high-level features that typically occurs in CNN top-down pathways. This module generates global context information, crucial for understanding relationships among diverse salient regions and ensuring comprehensive saliency detection.
3. Head Attention (HA) Module & Self Refinement (SR) Module: The HA module reduces redundancy by applying spatial and channel-wise attention, effectively enhancing the prominent spatial features. Meanwhile, the SR module further refines these features, ensuring heightened precision in the saliency maps.

Numerical and Qualitative Findings

The GCPANet model has been evaluated across six benchmark datasets, demonstrating superior performance against twelve state-of-the-art methods. In particular, the proposed model achieved the highest scores in F-measure, structural similarity, and the lowest Mean Absolute Error (MAE), illustrating its efficacy in salient object detection tasks.

Implications and Future Work

The integration of different feature levels and global contextual information showcases the potential of context-aware strategies in enhancing feature representation in CNNs. The proposed architecture displays promising adaptability to various image domains and challenging detection scenarios, such as cluttered backgrounds and interconnected salient regions.

Future research could explore extending this context-aware framework to other computer vision tasks, including instance segmentation and more complex scene understanding applications. Further investigation into optimizing computational resources for real-time applications could also be a prospective avenue following the insights derived from GCPANet.

In conclusion, GCPANet sets a new standard for salient object detection by integrating advanced feature aggregation and context flow mechanisms, underscoring the continuous evolution of deep learning architectures in understanding visual saliency.