Dense Outlier Detection and Open-Set Recognition Based on Training with Noisy Negative Images

The author proposes a novel approach for dense outlier detection and open-set recognition by training with noisy negative images, aiming to improve performance across various datasets. The shared features between semantic segmentation and outlier detection tasks greatly enhance the model's ability to recognize outliers without significantly impacting semantic segmentation accuracy.