重新评估物体可操作性分割的可复现性与尺度敏感性
Segmenting Object Affordances: Reproducibility and Sensitivity to Scale
- 构建可复现的实验框架，对比两种场景下的分割方法
- Mask2Former在多数测试集上表现最佳，但对尺度变化不鲁棒
- 提醒研究者注意训练与测试时物体分辨率差异的影响
视觉可操作性分割旨在识别物体上可交互的区域。现有方法多复用并改造语义分割的深度学习架构，且在小规模数据集上进行评估，但实验设置常不可复现，导致比较不公平且结果不一致。本文在两个单物体场景（无遮挡桌面物体和手持容器）下，采用可复现的实验设置对现有方法进行基准测试。我们重新训练了近期提出的Mask2Former模型用于可操作性分割，并发现其在两个场景的多数测试集上表现最优。分析表明，当物体分辨率与训练集不同时，模型性能显著下降，说明当前方法对尺度变化缺乏鲁棒性。
原文摘要 · Abstract (English)
Visual affordance segmentation identifies image regions of an object an agent can interact with. Existing methods re-use and adapt learning-based architectures for semantic segmentation to the affordance segmentation task and evaluate on small-size datasets. However, experimental setups are often not reproducible, thus leading to unfair and inconsistent comparisons. In this work, we benchmark these methods under a reproducible setup on two single objects scenarios, tabletop without occlusions and hand-held containers, to facilitate future comparisons. We include a version of a recent architecture, Mask2Former, re-trained for affordance segmentation and show that this model is the best-performing on most testing sets of both scenarios. Our analysis shows that models are not robust to scale variations when object resolutions differ from those in the training set.
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