Cascaded Partial Decoder for Fast and Accurate Salient Object Detection (1904.08739v1)

Abstract: Existing state-of-the-art salient object detection networks rely on aggregating multi-level features of pre-trained convolutional neural networks (CNNs). Compared to high-level features, low-level features contribute less to performance but cost more computations because of their larger spatial resolutions. In this paper, we propose a novel Cascaded Partial Decoder (CPD) framework for fast and accurate salient object detection. On the one hand, the framework constructs partial decoder which discards larger resolution features of shallower layers for acceleration. On the other hand, we observe that integrating features of deeper layers obtain relatively precise saliency map. Therefore we directly utilize generated saliency map to refine the features of backbone network. This strategy efficiently suppresses distractors in the features and significantly improves their representation ability. Experiments conducted on five benchmark datasets exhibit that the proposed model not only achieves state-of-the-art performance but also runs much faster than existing models. Besides, the proposed framework is further applied to improve existing multi-level feature aggregation models and significantly improve their efficiency and accuracy.

• The paper introduces the CPD framework that speeds up detection by discarding low-resolution features while maintaining high accuracy.
• The paper employs a holistic attention and fast context module to refine saliency maps, achieving superior F-measure and lower MAE compared to state-of-the-art methods.
• The paper’s cascaded optimization enables real-time application in tasks like autonomous driving and video analysis by balancing computational efficiency with detection performance.

Cascaded Partial Decoder for Fast and Accurate Salient Object Detection

The paper "Cascaded Partial Decoder for Fast and Accurate Salient Object Detection" introduces a novel framework aimed at enhancing the efficiency and accuracy of salient object detection (SOD). Authored by Zhe Wu, Li Su, and Qingming Huang from the University of Chinese Academy of Sciences and the Chinese Academy of Sciences, the paper addresses a critical need to balance computational demands with detection performance by innovatively leveraging deep learning techniques.

Summary

The primary contribution of this paper is the Cascaded Partial Decoder (CPD) framework, which emphasizes discarding high-resolution features from shallower layers to accelerate inference and improve computational efficiency. Traditional SOD networks, which aggregate multi-level features from convolutional neural networks (CNNs), often suffer from increased computational complexity due to the larger spatial resolutions of low-level features. The CPD framework proposes a bifurcated architecture that optimizes deeper layers' features to refine the salient object detection process.

Key Components

1. Cascaded Partial Decoder (CPD): The CPD discards lower-level features, which usually add computational cost without substantial performance benefits, while emphasizing higher-level features that generate precise saliency maps.
2. Holistic Attention Module: This module refines the initial saliency map to ensure it covers more useful information, thereby improving the representation of high-level features and suppressing distractors.
3. Fast and Efficient Context Module: Inspired by the receptive field block (RFB), this module abstracts discriminative features quickly with reduced computational complexity.
4. Cascaded Optimization Mechanism: The framework generates an initial saliency map that refines deeper features, leading to more accurate final saliency detection.

Experimental Evaluation

The efficacy of the CPD framework is validated across five benchmark datasets: ECSSD, HKU-IS, PASCAL-S, DUTS, and DUT-OMRON.

• Performance Metrics: The metrics used include mean absolute error (MAE) and F-measure (both maximum and average F-measures).
• Strong Numerical Results: The proposed model achieves superior performance, significantly outperforming existing state-of-the-art methods in both accuracy and processing speed. It achieves higher F-measure and lower MAE across the datasets.
• Comparison with Existing Models: Compared with methods like NLDF, DSS, BMPM, and PiCANet, the CPD framework consistently shows enhanced performance while running significantly faster.

Practical and Theoretical Implications

From a practical perspective, the CPD framework offers substantial improvements in real-time SOD applications where rapid processing is essential, such as in autonomous vehicles and real-time video analysis. The reduction in computational complexity while maintaining or even improving detection accuracy makes CPD a valuable asset for deploying deep learning models in resource-constrained environments.

Theoretically, this paper provides a compelling argument against the indiscriminate use of multi-level feature aggregation in CNN-based SOD models. By proving that high-resolution, low-level features contribute minimally to overall detection performance while significantly increasing computation costs, the paper sets a precedent for more selective feature utilization strategies in future research.

Future Directions

Potential future developments from this research could include:

1. Application to Other Dense Prediction Tasks: The framework's principles could be extended to other image segmentation tasks such as medical imaging and scene parsing.
2. Further Optimization and Generalization: Enhancements could be made to the holistic attention module and context module to further reduce computational demands and improve detection performance in various scenarios.
3. Exploration of Different Backbones: While this paper focuses on VGG16 and ResNet50, future research could explore the integration of CPD with other backbone networks to assess its generalizability and performance across different architectures.

In conclusion, this paper delivers a significant contribution to the field of salient object detection by introducing a cascaded partial decoder framework that combines efficiency with state-of-the-art accuracy. The meticulous experimental evaluation and practical relevance underscore its importance and potential impact on future SOD and related applications.