共显著性检测的免费午餐：上下文调整
Free Lunch for Co-Saliency Detection: Context Adjustment
摘要：
我们揭示了一个长期存在的问题，在流行的共同显着性检测系统：确实有训练和测试之间的不一致。构建高质量的共显着性检测数据集涉及耗时且劳动密集型的像素级标记，这迫使最近的工作依赖于语义分割或显着性检测数据集进行训练。然而，这些数据集中缺乏适当的共同显着性和多个前景对象的缺乏可能导致模型学习的虚假变化和固有偏差。为了解决这个问题，我们引入了通过上下文调整进行反事实训练的想法，并提出了一个“无成本”的组剪切粘贴（GCP）程序来利用现成的图像并合成新的样本。遵循GCP，我们收集了一个新的数据集，称为上下文调整训练（CAT）。CAT由33，500张图像组成，是当前共显性检测数据集的四倍。所有样本都自动使用高质量的遮罩注释、对象类别和边缘图进行注释。在最新的基准测试上进行了大量的实验，结果表明，CAT可以对各种最先进的模型进行大幅度的改进（5% ~ 25%）。我们希望我们数据集的规模、多样性和质量能够使该领域及其他领域的研究人员受益。我们的数据集将通过我们的项目页面公开访问。
英文摘要：
We unveil a long-standing problem in the prevailing co-saliency detection systems: there is indeed inconsistency between training and testing. Constructing a high-quality co-saliency detection dataset involves time-consuming and labor-intensive pixel-level labeling, which has forced most recent works to rely instead on semantic segmentation or saliency detection datasets for training. However, the lack of proper co-saliency and the absence of multiple foreground objects in these datasets can lead to spurious variations and inherent biases learned by models. To tackle this, we introduce the idea of counterfactual training through context adjustment and propose a "cost-free" group-cut-paste (GCP) procedure to leverage off-the-shelf images and synthesize new samples. Following GCP, we collect a novel dataset called Context Adjustment Training (CAT). CAT consists of 33,500 images, which is four times larger than the current co-saliency detection datasets. All samples are automatically annotated with high-quality mask annotations, object categories, and edge maps. Extensive experiments on recent benchmarks are conducted, show that CAT can improve various state-of-the-art models by a large margin (5% ~ 25%). We hope that the scale, diversity, and quality of our dataset can benefit researchers in this area and beyond. Our dataset will be publicly accessible through our project page.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
