A Simple Pooling-Based Design for Real-Time Salient Object Detection (1904.09569v1)

Abstract: We solve the problem of salient object detection by investigating how to expand the role of pooling in convolutional neural networks. Based on the U-shape architecture, we first build a global guidance module (GGM) upon the bottom-up pathway, aiming at providing layers at different feature levels the location information of potential salient objects. We further design a feature aggregation module (FAM) to make the coarse-level semantic information well fused with the fine-level features from the top-down pathway. By adding FAMs after the fusion operations in the top-down pathway, coarse-level features from the GGM can be seamlessly merged with features at various scales. These two pooling-based modules allow the high-level semantic features to be progressively refined, yielding detail enriched saliency maps. Experiment results show that our proposed approach can more accurately locate the salient objects with sharpened details and hence substantially improve the performance compared to the previous state-of-the-arts. Our approach is fast as well and can run at a speed of more than 30 FPS when processing a $300 \times 400$ image. Code can be found at http://mmcheng.net/poolnet/.

A Simple Pooling-Based Design for Real-Time Salient Object Detection

The paper "A Simple Pooling-Based Design for Real-Time Salient Object Detection" presents significant advancements in the field of salient object detection (SOD) by leveraging simple pooling techniques. The authors introduce two novel modules—Global Guidance Module (GGM) and Feature Aggregation Module (FAM)—both built upon the foundational U-shape architecture of Feature Pyramid Networks (FPNs).

Contributions and Methodology

The first notable contribution is the Global Guidance Module (GGM) designed to enhance the location information of potential salient objects across different feature levels in a convolutional neural network (CNN). The GGM combines a Pyramid Pooling Module (PPM) with Global Guiding Flows (GGFs). The PPM captures global context information, which is crucial for understanding the overall structure and layout of the image, while the GGFs deliver high-level semantic information across all pyramid levels. This method addresses the dilution problem of high-level semantic information as it propagates through the network, ensuring that the location information remains intact.

The second major contribution is the Feature Aggregation Module (FAM). Integrated after the fusion operations in the top-down pathway, the FAM facilitates seamless merging of coarse-level features from the GGM with fine-level details obtained from shallower layers. This module is essential in improving the network's capacity to capture local context information at various scales, thereby enhancing the detail and accuracy of the generated saliency maps.

Results and Performance

The experimental results demonstrate that PoolNet, the proposed approach, significantly outperforms existing state-of-the-art methods across multiple benchmarks (ECSSD, PASCAL-S, DUT-OMRON, HKU-IS, SOD, and DUTS), showcasing enhanced precision and recall metrics. For instance, on the HKU-IS dataset, PoolNet achieves a maximum F-measure score of 0.936 and a mean absolute error (MAE) of 0.032 with the VGG-16 backbone, an improvement indicative of the model's robustness. Furthermore, the network demonstrates real-time capabilities, processing images at over 30 frames per second on a standard NVIDIA Titan Xp GPU, which is considerably faster than many existing methodologies.

Practical and Theoretical Implications

Practically, the findings from PoolNet suggest that simple pooling techniques can significantly boost the performance of salient object detection models without the need for complex architectures or excessive computational resources. This is particularly relevant for applications requiring real-time processing, such as autonomous driving, robotics, and interactive systems. The addition of an edge detection branch further refines the salient maps, ensuring that object boundaries are sharp and well-defined, which is crucial for tasks involving object segmentation and recognition.

Theoretically, the paper opens new avenues for exploring the role of pooling in enhancing feature extraction and fusion in deep learning models. The modular nature of GGM and FAM implies that these components can be adapted and incorporated into other pyramid-based CNN architectures, potentially benefiting a wide range of computer vision tasks beyond SOD, such as semantic segmentation and object detection.

Future Directions

Future research could investigate the applicability of the GGM and FAM modules in different neural network architectures beyond the U-shape design. Additionally, the exploration of advanced pooling techniques and their integration with attention mechanisms might further enhance the detection capabilities. Extending the joint training approach with other auxiliary tasks relevant to SOD could also provide deeper insights into the multi-task learning paradigm.

In conclusion, the paper successfully demonstrates that effectively designed pooling-based modules can significantly enhance both the accuracy and efficiency of salient object detection systems. This work not only provides a solid foundation for real-time SOD but also paves the way for future advancements in the domain of computer vision.