Decouple Defogging and Semantic Learning for Improved Foggy Domain-Adaptive Segmentation

Grunnleggende konsepter

A novel training framework, Decouple Defogging and Semantic learning (D2SL), that learns better semantics for segmentation while maintaining defogging ability by decoupling defogging and semantic learning tasks.

Sammendrag

The paper investigates domain adaptive semantic segmentation in foggy weather scenarios, which aims to enhance the utilization of unlabeled foggy data and improve the model's adaptability to foggy conditions. Current methods rely on clear images as references and jointly learn defogging and segmentation for foggy images, which has two main drawbacks: (1) the coupling of segmentation and defogging feature representations, resulting in a decrease in semantic representation capability, and (2) the failure to leverage real fog priors in unlabeled foggy data, leading to insufficient model generalization ability.

To address these issues, the authors propose the D2SL framework, which decouples defogging and semantic learning tasks. D2SL introduces a Domain-Consistent Transfer (DCT) strategy to establish a connection between defogging and segmentation tasks, and a Real Fog Transfer (RFT) strategy to improve defogging effects by fully leveraging the fog priors from real foggy images. This enhances the semantic representations required for segmentation during the defogging learning process and maximizes the representation capability of fog invariance by effectively utilizing real fog data.

Comprehensive experiments validate the effectiveness of the proposed method, demonstrating that D2SL consistently delivers robust performance across various domain-adaptive tasks in foggy conditions and outperforms contemporary methods.

Statistikk

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Sitater

None

Viktige innsikter hentet fra

D2SL

by Xuan Sun,Zha... klokken arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04807.pdf

Dypere Spørsmål

How can the proposed D2SL framework be extended to other domain adaptation tasks beyond semantic segmentation in foggy conditions

The D2SL framework, which focuses on decoupling defogging and semantic learning for foggy domain-adaptive segmentation, can be extended to various other domain adaptation tasks beyond semantic segmentation in foggy conditions. One potential extension could be in the field of autonomous driving, where adapting to different weather conditions is crucial for the safety and efficiency of the system. By incorporating additional data sources related to different weather conditions such as rain, snow, or night-time driving, the D2SL framework could be adapted to learn robust features that are invariant to these varying conditions. This extension would involve training the model on a diverse dataset that includes labeled data from different weather scenarios, allowing the model to generalize well to unseen conditions. By incorporating domain-consistent transfer and real data transfer strategies tailored to these new domains, the model can effectively adapt to a wider range of environmental factors encountered in real-world applications.

What are the potential limitations of the DCT and RFT strategies, and how could they be further improved to enhance the model's generalization capabilities

While the Domain-Consistent Transfer (DCT) and Real Fog Transfer (RFT) strategies in the D2SL framework have shown promising results in enhancing the model's adaptability to foggy conditions, there are potential limitations that could be further improved. One limitation of the DCT strategy is that it may not fully capture the complex relationships between defogging and semantic learning features in all scenarios. To address this, incorporating more advanced alignment techniques or introducing additional regularization methods could help improve the alignment between the two tasks. Similarly, the RFT strategy may face challenges in effectively leveraging real fog priors from diverse real-world data sources. Enhancements such as incorporating multi-modal data fusion techniques or exploring unsupervised learning methods to extract fog priors more effectively could further enhance the model's generalization capabilities.

Given the importance of leveraging real fog priors, how could the D2SL framework be adapted to incorporate other types of real-world data, such as diverse weather conditions, to improve its robustness

To incorporate other types of real-world data, such as diverse weather conditions, into the D2SL framework to improve its robustness, several adaptations can be considered. One approach could involve collecting a comprehensive dataset that includes labeled images from various weather conditions, such as rain, snow, fog, and clear weather. By training the model on this diverse dataset, the framework can learn to adapt to different weather scenarios effectively. Additionally, modifying the Real Fog Transfer (RFT) strategy to incorporate priors specific to different weather conditions could enhance the model's ability to generalize across a broader range of environmental factors. By extending the framework to incorporate multi-domain adaptation techniques and leveraging a more extensive range of real-world data sources, the D2SL framework can be adapted to handle diverse weather conditions with improved robustness and generalization capabilities.