מושגי ליבה
The proposed Deep Channel Prior (DCP) and Unsupervised Feature Enhancement Module (UFEM) can effectively boost the performance of pre-trained visual recognition models in real-world degraded conditions, such as fog, low-light, and motion blur, by restoring latent content, removing artifacts, and modulating global feature correlations in an unsupervised manner.
תקציר
The paper proposes a novel Deep Channel Prior (DCP) and an Unsupervised Feature Enhancement Module (UFEM) to improve the robustness of visual recognition models for autonomous driving in real-world degraded conditions.
Key highlights:
- The authors observe that in the deep representation space, the channel correlations of degraded features with the same degradation type have uniform distribution, which can be leveraged to facilitate the mapping relationship learning between degraded and clear representations.
- The UFEM is designed with a two-stage architecture. The first stage uses a dual-learning architecture with a multi-adversarial mechanism to restore latent content and remove artifacts in degraded features. The second stage further refines the features by modulating their global channel correlations guided by the DCP.
- Extensive evaluations on image classification, object detection, and semantic segmentation tasks across synthetic and real-world degradation datasets demonstrate the effectiveness of the proposed method in comprehensively improving the performance of pre-trained models in real-world degraded conditions.
סטטיסטיקה
The paper does not provide specific numerical data or statistics in the main text. The focus is on the proposed methodology and its evaluation on various benchmark datasets.
ציטוטים
The paper does not contain any striking quotes that support the key logics.