ExFuse: Enhancing Feature Fusion for Semantic Segmentation (1804.03821v1)

Abstract: Modern semantic segmentation frameworks usually combine low-level and high-level features from pre-trained backbone convolutional models to boost performance. In this paper, we first point out that a simple fusion of low-level and high-level features could be less effective because of the gap in semantic levels and spatial resolution. We find that introducing semantic information into low-level features and high-resolution details into high-level features is more effective for the later fusion. Based on this observation, we propose a new framework, named ExFuse, to bridge the gap between low-level and high-level features thus significantly improve the segmentation quality by 4.0\% in total. Furthermore, we evaluate our approach on the challenging PASCAL VOC 2012 segmentation benchmark and achieve 87.9\% mean IoU, which outperforms the previous state-of-the-art results.

• The paper introduces ExFuse, a framework that addresses semantic and resolution gaps by embedding semantic cues in low-level features.
• The methodology employs semantic supervision, explicit resolution embedding, and layer rearrangement to enhance segmentation performance with a reported 87.9% mIoU.
• The approach offers practical value for applications such as autonomous driving and medical imaging while paving the way for adaptive feature embedding research.

ExFuse: Enhancing Feature Fusion for Semantic Segmentation

The paper "ExFuse: Enhancing Feature Fusion for Semantic Segmentation" introduces an advanced framework to address the challenge of effective feature fusion in semantic segmentation networks. The research emphasizes the issues with the conventional approach of feature fusion, where low-level, high-resolution, and high-level, low-resolution features are combined. This method often results in suboptimal performance due to intrinsic semantic and resolution disparities between the two extremes.

Core Contributions and Methodology

The authors present a novel framework named ExFuse to ameliorate these challenges by bridging the semantic and resolution gaps between feature layers. The key strategies employed in ExFuse include:

1. Embedding Semantic Information into Low-level Features: The framework integrates methods such as semantic supervision and semantic embedding branch to inject semantic information into low-level features. These approaches enrich low-level layers with semantic cues, making them more aligned with high-level abstractions during the fusion process.
2. Embedding High-resolution Details into High-level Features: ExFuse utilizes techniques like explicit channel resolution embedding (ECRE) to ensure high-level features are augmented with spatial details. The densely adjacent prediction (DAP) mechanism further enhances spatial accuracy by making each feature map channel predict the semantics of neighboring pixels, increasing the spatial encoding within the channel.
3. Layer Rearrangement: By modifying the order of layers in the encoder network, ExFuse strategically places more network depth in earlier stages. This shift promotes better feature representation at lower levels, effectively enhancing the granularity required for precise segmentation.

Experimental Results and Implications

Experimentation on the PASCAL VOC 2012 segmentation benchmark reveals that ExFuse achieved a mean Intersection over Union (mIoU) of 87.9%, outperforming the previous state-of-the-art models. Notably, the proposed technique shows significant performance improvements by addressing the ineffective feature fusion evident in traditional U-Net-like architectures. Through a systematic approach that embeds semantic resolution throughout the network, ExFuse significantly enhances segmentation results by 4.0%, showcasing the potential of using feature enhancement strategies in semantic segmentation networks.

Theoretical and Practical Implications

From a theoretical standpoint, this paper provides insights into the architectural design of segmentation networks, emphasizing the importance of balanced semantic and spatial information across network hierarchies. Practically, the ExFuse framework demonstrates its efficacy in improving semantic segmentation tasks, indicative of its applicability in real-world scenarios such as autonomous driving and medical image analysis, where precise segmentation is critical.

Future Directions

Looking ahead, the concepts introduced by ExFuse could inspire further research into more dynamic and adaptive feature embedding techniques. The idea of bridging feature gaps can potentially be extended to other areas of vision beyond segmentation, including object detection and scene understanding. Integrating such methods with emerging neural architectures could advance the development of more robust and efficient visual recognition systems. Furthermore, exploring lightweight versions of ExFuse suited for deployment in resource-constrained environments such as mobile and embedded systems could be a fruitful avenue for future work.

In summary, the ExFuse framework presents a well-founded advancement in the domain of semantic segmentation, underscoring the importance of feature fusion in enhancing model performance. Its strategic methodologies provide a promising path forward for tackling feature-level discrepancies in convolutional neural networks.