# Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation Source: [Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:32:19.733Z Authors: Tong Shen, Guosheng Lin, Chunhua Shen, Ian Reid Published: 2017-01-25T01:11:43.000Z ## Abstract Semantic image segmentation is a fundamental task in image understanding. Per-pixel semantic labelling of an image benefits greatly from the ability to consider region consistency both locally and globally. However, many Fully Convolutional Network based methods do not impose such consistency, which may give rise to noisy and implausible predictions. We address this issue by proposing a dense multi-label network module that is able to encourage the region consistency at different levels. This simple but effective module can be easily integrated into any semantic segmentation systems. With comprehensive experiments, we show that the dense multi-label can successfully remove the implausible labels and clear the confusion so as to boost the performance of semantic segmentation systems. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()