论文标题
关于语义细分的鲁棒性的表示结构
On the Structures of Representation for the Robustness of Semantic Segmentation to Input Corruption
论文作者
论文摘要
语义细分是一项场景理解任务，这是安全至关重要应用的核心，在这种情况下，对损坏的投入至关重要。隐式背景估计（IBE）已证明是一种有希望的技术，可以提高语义分割模型的分布外输入的鲁棒性，几乎没有成本。在本文中，我们提供了分析，比较了使用SoftMax，IBE和Sigmoid的优化目标所学的结构，以提高其了解它们与鲁棒性的关系。通过此分析，我们提出将Sigmoid与IBE（抄写员）相结合以提高鲁棒性。最后，我们证明了抄写员在所有腐败和严重程度上均以42.1的损坏和严重程度汇总分段性能，而IBE 40.3和SoftMax基线37.5则表现出较高的分段性能。
Semantic segmentation is a scene understanding task at the heart of safety-critical applications where robustness to corrupted inputs is essential. Implicit Background Estimation (IBE) has demonstrated to be a promising technique to improve the robustness to out-of-distribution inputs for semantic segmentation models for little to no cost. In this paper, we provide analysis comparing the structures learned as a result of optimization objectives that use Softmax, IBE, and Sigmoid in order to improve understanding their relationship to robustness. As a result of this analysis, we propose combining Sigmoid with IBE (SCrIBE) to improve robustness. Finally, we demonstrate that SCrIBE exhibits superior segmentation performance aggregated across all corruptions and severity levels with a mIOU of 42.1 compared to both IBE 40.3 and the Softmax Baseline 37.5.
