Path Aggregation Network for Instance Segmentation (1803.01534v4)

Abstract: The way that information propagates in neural networks is of great importance. In this paper, we propose Path Aggregation Network (PANet) aiming at boosting information flow in proposal-based instance segmentation framework. Specifically, we enhance the entire feature hierarchy with accurate localization signals in lower layers by bottom-up path augmentation, which shortens the information path between lower layers and topmost feature. We present adaptive feature pooling, which links feature grid and all feature levels to make useful information in each feature level propagate directly to following proposal subnetworks. A complementary branch capturing different views for each proposal is created to further improve mask prediction. These improvements are simple to implement, with subtle extra computational overhead. Our PANet reaches the 1st place in the COCO 2017 Challenge Instance Segmentation task and the 2nd place in Object Detection task without large-batch training. It is also state-of-the-art on MVD and Cityscapes. Code is available at https://github.com/ShuLiu1993/PANet

• The paper introduces bottom-up path augmentation to shorten feature paths and improve localization accuracy in instance segmentation.
• The paper implements adaptive feature pooling to integrate multi-level features, ensuring each proposal benefits from rich information.
• The paper leverages fully-connected fusion to combine global and local cues, achieving superior mask prediction and state-of-the-art performance.

Path Aggregation Network for Instance Segmentation

The paper "Path Aggregation Network for Instance Segmentation" introduces a novel approach to enhance the performance of proposal-based instance segmentation frameworks. The proposed method, Path Aggregation Network (PANet), aims to improve information flow and feature utilization by implementing three key components: bottom-up path augmentation, adaptive feature pooling, and fully-connected fusion. These improvements are straightforward to implement and impose only minor computational overhead.

Core Contributions

1. Bottom-Up Path Augmentation: The authors address the limitation in Mask R-CNN where long paths from low-level features to topmost layers result in potential loss of localization accuracy. They propose adding a bottom-up path augmentation to propagate accurate localization signals from lower to higher layers. This augmentation creates a shorter path across the feature hierarchy, enhancing the entire feature pyramid with strong localization cues from lower levels.
2. Adaptive Feature Pooling: The conventional proposal assignment in FPN assigns proposals to a single feature level based on their size, which could lead to loss of valuable information from other levels. To overcome this insufficiency, PANet pools features from all levels and uses a fusion operation (e.g., element-wise max or sum) to integrate these features. This adaptive pooling ensures that each proposal benefits from the rich, multi-level information available within the network.
3. Fully-Connected Fusion: Recognizing the complementary strengths of fully-connected layers versus convolutional layers, the authors enhance the mask prediction branch by fusing outputs from both types of layers. Convolutions capture local information with shared parameters, whereas fully-connected layers are location-sensitive and can utilize global information. By integrating these predictions, PANet achieves more accurate and higher quality masks.

Experimental Results

PANet demonstrates state-of-the-art performance across several challenging datasets. On the COCO dataset, it outperforms the previous best systems in instance segmentation and object detection without resorting to large-batch training. On Cityscapes and MVD datasets, PANet achieves top-ranking results. Its effectiveness is reflected in the improvement over baseline Mask R-CNN across multiple evaluation metrics — including AP, AP$_{50}$, and AP$_{75}$ — on the COCO dataset.

Key Numerical Results

• COCO Dataset: PANet achieved document-ending performance with ResNeXt-101 as the backbone, resulting in 42.0% AP in instance segmentation and 47.4% AP in object detection.
• Cityscapes Dataset: It obtained 36.4% AP on the test subset, setting a new benchmark in this domain.
• MVD: PANet reached an AP of 26.3% on the test subset, showing significant improvements over previous methods.

Practical and Theoretical Implications

The proposed approach, with its enhanced feature propagation, is particularly beneficial for tasks requiring high localization accuracy, such as autonomous driving and video surveillance. By leveraging bottom-up path augmentation and adaptive feature integration, the PANet effectively maximizes the utility of available information, thus improving the robustness and accuracy of instance segmentation models.

Theoretically, these enhancements challenge the traditional ways of handling feature pyramids in neural networks. They suggest that multi-level feature aggregation and adaptive pooling can lead to significant performance boosts in deep learning models focused on object recognition tasks. This methodology is generalizable and can be extended to other architectures and datasets, providing a robust framework for future research in computer vision.

Future Directions

Looking ahead, potential advances could include applying PANet to video and RGBD data, where temporal information and depth cues add complexity but also rich information. Furthermore, exploring different fusion strategies and integrating advanced backbone networks like EfficientNet or Transformer models may yield even better results.

In conclusion, the PANet framework represents a substantial step forward in instance segmentation, demonstrating that thoughtful aggregation and propagation of features across different levels can significantly enhance the performance of deep learning models in intricate object detection tasks.