构建新数据集评估遮挡对全景分割的影响，提出抗遮挡训练方法。
COCO-OLAC: A Benchmark for Occluded Panoptic Segmentation and Image Understanding
- 基于COCO构建遮挡标注数据集，区分三种遮挡程度。
- 遮挡越严重，模型性能越差，最高降幅达15.3点。
- 用对比学习融合遮挡信息，提升模型鲁棒性，达到新最佳表现。
为解决全景分割与图像理解中的遮挡问题，本文提出一个名为COCO-OLAC（COCO遮挡标签用于所有计算机视觉任务）的大规模新数据集，该数据集源自COCO，通过人工标注将图像分为三种感知遮挡等级。利用COCO-OLAC，我们系统评估并量化了遮挡对全景分割在不同遮挡程度样本上的影响。与当前最优模型的对比实验表明，遮挡显著降低性能，遮挡越严重，表现越差。此外，本文提出一种简单但有效的初步方法，通过对比学习利用遮挡标注信息，使模型学习更具鲁棒性的表示以捕捉不同遮挡强度。实验结果表明，该方法提升了基线模型性能，并在提出的COCO-OLAC数据集上达到了当前最优水平。
原文摘要 · Abstract (English)
To help address the occlusion problem in panoptic segmentation and image understanding, this paper proposes a new large-scale dataset named COCO-OLAC (COCO Occlusion Labels for All Computer Vision Tasks), which is derived from the COCO dataset by manually labelling images into three perceived occlusion levels. Using COCO-OLAC, we systematically assess and quantify the impact of occlusion on panoptic segmentation on samples having different levels of occlusion. Comparative experiments with SOTA panoptic models demonstrate that the presence of occlusion significantly affects performance, with higher occlusion levels resulting in notably poorer performance. Additionally, we propose a straightforward yet effective method as an initial attempt to leverage the occlusion annotation using contrastive learning to render a model that learns a more robust representation capturing different severities of occlusion. Experimental results demonstrate that the proposed approach boosts the performance of the baseline model and achieves SOTA performance on the proposed COCO-OLAC dataset.
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