Semantic Segmentation On Nighttime Driving

Metrics

mIoU

Results

Performance results of various models on this benchmark

		Paper Title
TADP	60.8	Text-image Alignment for Diffusion-based Perception
CoDA	59.2	CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning
Refign (HRDA)	58.0	Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions
Refign (DAFormer)	56.8	Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions
MGCDA	49.4	Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
DANNet (PSPNet)	47.70	DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation
GCMA	45.6	Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
ERF-PSPNet	45.09	See Clearer at Night: Towards Robust Nighttime Semantic Segmentation through Day-Night Image Conversion
DANNet (DeepLab-v2)	44.98	DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation
DANNet (RefineNet)	42.36	DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation
CIConv	41.6	Zero-Shot Day-Night Domain Adaptation with a Physics Prior
DMAda	36.1	Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime

0 of 12 row(s) selected.