All-day Semantic Segmentation
4 papers with code • 1 benchmarks • 1 datasets
Semantic segmentation under all-day conditions
Most implemented papers
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
IBN-Net carefully integrates Instance Normalization (IN) and Batch Normalization (BN) as building blocks, and can be wrapped into many advanced deep networks to improve their performances.
Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes
The proposed deep dual-resolution networks (DDRNets) are composed of two deep branches between which multiple bilateral fusions are performed.
RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening
Enhancing the generalization capability of deep neural networks to unseen domains is crucial for safety-critical applications in the real world such as autonomous driving.
Interactive Learning of Intrinsic and Extrinsic Properties for All-day Semantic Segmentation
In this paper, in contrast to existing methods, we tackle this challenge from the perspective of image formulation itself, where the image appearance is determined by both intrinsic (e. g., semantic category, structure) and extrinsic (e. g., lighting) properties.