# Depth Edge Alignment Loss: DEALing with Depth in Weakly Supervised Semantic Segmentation Source: [Depth Edge Alignment Loss: DEALing with Depth in Weakly Supervised Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2026-01-17T05:00:05.253Z Authors: Patrick Schmidt, Vasileios Belagiannis, Lazaros Nalpantidis Published: 2025-09-22T12:42:10.000Z ## Abstract Autonomous robotic systems applied to new domains require an abundance of expensive, pixel-level dense labels to train robust semantic segmentation models under full supervision. This study proposes a model-agnostic Depth Edge Alignment Loss to improve Weakly Supervised Semantic Segmentation models across different datasets. The methodology generates pixel-level semantic labels from image-level supervision, avoiding expensive annotation processes. While weak supervision is widely explored in traditional computer vision, our approach adds supervision with pixel-level depth information, a modality commonly available in robotic systems. We demonstrate how our approach improves segmentation performance across datasets and models, but can also be combined with other losses for even better performance, with improvements up to +5.439, +1.274 and +16.416 points in mean Intersection over Union on the PASCAL VOC / MS COCO validation, and the HOPE static onboarding split, respectively. Our code is made publicly available at https://github.com/DTU-PAS/DEAL. ## 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]()