BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation (1808.00897v1)

Abstract: Semantic segmentation requires both rich spatial information and sizeable receptive field. However, modern approaches usually compromise spatial resolution to achieve real-time inference speed, which leads to poor performance. In this paper, we address this dilemma with a novel Bilateral Segmentation Network (BiSeNet). We first design a Spatial Path with a small stride to preserve the spatial information and generate high-resolution features. Meanwhile, a Context Path with a fast downsampling strategy is employed to obtain sufficient receptive field. On top of the two paths, we introduce a new Feature Fusion Module to combine features efficiently. The proposed architecture makes a right balance between the speed and segmentation performance on Cityscapes, CamVid, and COCO-Stuff datasets. Specifically, for a 2048x1024 input, we achieve 68.4% Mean IOU on the Cityscapes test dataset with speed of 105 FPS on one NVIDIA Titan XP card, which is significantly faster than the existing methods with comparable performance.

• The paper introduces a dual-path structure that decouples spatial detail retention from context capture, achieving real-time performance at 105 FPS.
• It integrates a Feature Fusion Module and an Attention Refinement Module to effectively combine detailed spatial and broad contextual information.
• Extensive experiments on Cityscapes, CamVid, and COCO-Stuff validate its superior accuracy and speed for real-time semantic segmentation.

An Analytical Overview of "BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation"

This essay explores the paper entitled "BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation" by Changqian Yu et al., presenting a thorough analysis suited to experienced researchers in the field. The research primarily addresses the challenging problem of achieving real-time speed in semantic segmentation without compromising the accuracy of high-resolution spatial details and contextual information.

Overview

Semantic segmentation is pivotal for numerous computer vision applications such as autonomous driving and augmented reality, necessitating both effective speed and high accuracy. However, traditionally, enhancing one often degrades the other. The Bilateral Segmentation Network (BiSeNet) presented in this paper proposes a novel architecture to resolve this trade-off by introducing dual pathways: a Spatial Path (SP) and a Context Path (CP).

Architectural Design

Spatial Path

The Spatial Path (SP) component is designed to retain high spatial resolution. This path integrates three convolutional layers with a small stride to preserve spatial details, generating high-resolution feature maps. This design choice is beneficial for tasks requiring fine-grained spatial details like semantic segmentation, which involves labeling each pixel in an image.

Context Path

The Context Path (CP), on the other hand, employs a fast downsampling strategy using a lightweight backbone, specifically a modified Xception model termed Xception39. This path is augmented with a global average pooling layer to further expand the receptive field, enabling the network to capture extensive contextual information.

Feature Fusion Module and Attention Refinement Module

A novel Feature Fusion Module (FFM) is proposed to amalgamate the outputs of the two paths efficiently. Due to the distinct nature of the features from the SP (detailed spatial information) and CP (broader contextual information), the concatenation and subsequent re-weighting via FFM are critical for optimal results. The Attention Refinement Module (ARM) is also introduced to refine the features in the CP, leveraging global context to enhance attention maps further.

Numerical Results and Performance

The paper presents several notable numerical results. Specifically, BiSeNet achieves a Mean Intersection over Union (Mean IOU) of 68.4% on the Cityscapes test dataset at a processing speed of 105 FPS on an NVIDIA Titan XP card for a 2048×1024 input image. This performance marks a significant improvement over existing methods, both in terms of speed and accuracy, making BiSeNet an effective solution for real-time applications.

BiSeNet's efficacy is further demonstrated through extensive benchmarking on the Cityscapes, CamVid, and COCO-Stuff datasets. For instance, on the CamVid dataset, the network achieves a Mean IOU of 65.6% and 68.7% using Xception39 and ResNet18 as backbones, respectively. On the COCO-Stuff validation set, BiSeNet delivers a Mean IOU of 31.3%, showcasing its adaptability to various datasets and tasks.

Implications and Future Directions

The practical implications of this research are profound. By providing a robust solution to the real-time semantic segmentation dilemma, BiSeNet enhances the capabilities of systems requiring rapid, detailed scene analysis. In applications like autonomous driving, where speed and accuracy are not merely preferable but crucial, BiSeNet offers a potentially transformative approach.

From a theoretical perspective, this research underscores the viability of decoupling spatial information retention and contextual field expansion within a unified framework, encouraging future exploration into similar bifurcated model architectures. Moreover, the paper’s integration of FFM and ARM highlights the ongoing trend of employing attention mechanisms and feature fusion strategies to bolster network performance.

Conclusion

Changqian Yu et al. have presented a compelling paper with BiSeNet, effectively addressing the intricate balance between speed and accuracy in real-time semantic segmentation. The innovative dual-path architecture, combined with strategic modules like FFM and ARM, demonstrates substantial improvements over existing methodologies. The implications of this research span both theoretical advancements in model architecture design and practical enhancements in critical computer vision applications, paving the way for future developments in real-time AI systems.