评测分割模型在真实扰动和未知场景下的可靠性，揭示大模型预训练的重要性。
The BRAVO Semantic Segmentation Challenge Results in UNCV2024
- 统一挑战赛评估模型在扰动和未知类别下的表现
- 大规模预训练显著提升模型可靠性，架构越简越好
- 适合关注模型鲁棒性与真实应用的开发者
我们提出统一的BRAVO挑战，用于评估语义分割模型在真实扰动和未知分布（OOD）场景下的可靠性。定义两类可靠性：(1) 语义可靠性，衡量模型在各类扰动下的准确率与校准度；(2) OOD可靠性，评估模型对训练中未见类别的检测能力。挑战吸引了近100个来自国际知名研究机构的团队提交方案。结果揭示了大规模预训练与极简架构设计在构建稳健可靠分割模型中的关键作用。
原文摘要 · Abstract (English)
We propose the unified BRAVO challenge to benchmark the reliability of semantic segmentation models under realistic perturbations and unknown out-of-distribution (OOD) scenarios. We define two categories of reliability: (1) semantic reliability, which reflects the model's accuracy and calibration when exposed to various perturbations; and (2) OOD reliability, which measures the model's ability to detect object classes that are unknown during training. The challenge attracted nearly 100 submissions from international teams representing notable research institutions. The results reveal interesting insights into the importance of large-scale pre-training and minimal architectural design in developing robust and reliable semantic segmentation models.
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