Boundary-Aware Feature Propagation for Scene Segmentation (1909.00179v1)

Abstract: In this work, we address the challenging issue of scene segmentation. To increase the feature similarity of the same object while keeping the feature discrimination of different objects, we explore to propagate information throughout the image under the control of objects' boundaries. To this end, we first propose to learn the boundary as an additional semantic class to enable the network to be aware of the boundary layout. Then, we propose unidirectional acyclic graphs (UAGs) to model the function of undirected cyclic graphs (UCGs), which structurize the image via building graphic pixel-by-pixel connections, in an efficient and effective way. Furthermore, we propose a boundary-aware feature propagation (BFP) module to harvest and propagate the local features within their regions isolated by the learned boundaries in the UAG-structured image. The proposed BFP is capable of splitting the feature propagation into a set of semantic groups via building strong connections among the same segment region but weak connections between different segment regions. Without bells and whistles, our approach achieves new state-of-the-art segmentation performance on three challenging semantic segmentation datasets, i.e., PASCAL-Context, CamVid, and Cityscapes.

• The paper introduces a novel method that embeds semantic boundary information into feature propagation, improving pixel-wise segmentation precision.
• The paper utilizes Unidirectional Acyclic Graphs (UAGs) to streamline feature propagation, significantly reducing computational complexity for high-resolution images.
• The paper demonstrates state-of-the-art performance with a mIoU of 53.6% on PASCAL-Context, paving the way for efficient real-time scene segmentation.

Boundary-Aware Feature Propagation for Scene Segmentation: A Professional Overview

This paper addresses the complex task of scene segmentation by introducing a novel methodology centered on boundary-aware feature propagation. The primary contribution is embedding semantic boundary information into the feature propagation process, expertly improving the discriminative power between objects while enhancing feature similarity within an object. This innovative approach achieves high performance on established datasets, marking a substantial advancement in semantic segmentation.

Methodological Advancements

The paper proposes an advanced technique of learning boundaries as an additional semantic class, effectively incorporating this into neural networks without extensive architectural changes. This reformation introduces a new layer of boundary awareness, enabling the network to adjudicate pixel classification more finely. The method involves two crucial components:

1. Unidirectional Acyclic Graphs (UAGs): These are employed to model the feature propagation efficiently, replacing the traditional and computationally intensive undirected cyclic graphs (UCGs). UAGs facilitate faster processing by enabling pixel interactions in parallel dimensions, significantly reducing time complexity, particularly with high-resolution images. This structural innovation speeds up segmentation tasks and maintains high accuracy.
2. Boundary-Aware Feature Propagation (BFP): This module intelligently regulates information flow based on learned boundaries, allowing deep learning models to segment continuous image regions as distinct objects. The boundary detection process relies on propagating confidence signals derived from boundary probabilities, thereby controlling the extent to which features of different region segments interact.

Evaluation and Performance

The high efficiency of the proposed architecture is empirically validated across multiple datasets, including PASCAL-Context, CamVid, and Cityscapes. The results indicate a new state-of-the-art performance:

• The Boundary-Aware Feature Propagation network improves the mean Intersection-over-Union (mIoU) scores significantly. For instance, an improvement baseline in the PASCAL-Context dataset reached up to 53.6% mIoU, outperforming previous benchmarks like EncNet and PSPNet.
• Computational efficiency is prominently highlighted, as the UAGs reduce the number of operational loops dramatically compared to DAGs. This benefits not only training and inferencing speeds but also resource efficiency, making the model suitable for applications demanding real-time processing.

Implications and Future Work

The introduction of semantic boundaries as a learning class paves the way for enhanced interaction in convolutional networks, offering significant improvements in scene segmentation's fidelity and precision. The potential applications range from autonomous driving systems to advanced image recognition tasks in complex visual environments.

Looking forward, the methodology could extend beyond simple segmentation into more granular applications like instance segmentation and object detection in real-time environments. Forthcoming developments may integrate more sophisticated attention mechanisms or hybrid modeling approaches that merge temporal data with spatial segmentation, further broadening the impact of this foundational research in artificial intelligence and computer vision.

In conclusion, this paper presents a robust solution to the longstanding challenges in scene segmentation, providing a scalable framework that enhances both the accuracy and efficiency of feature propagation in convolutional neural networks. The advancements outlined have significant implications for both practical implementations and theoretical explorations in AI-driven image analysis.