CAT: Context Adjustment Training Dataset

CAT is a specialized dataset for co-saliency detection. This dataset is intended for both helping to assess the performance of vision algorithms and supporting research that aims to exploit large volumes of annotated data, e.g., for training deep neural networks.

Scale & Features
- A total number of 33500 image samples.
- 280 semantic groups affiliated to 15 superclasses.
- High-quality mask annotations.
- Diverse visual context with multiple foreground objects.

CAT 是一款针对共显著性检测的专用数据集。该数据集旨在协助评估视觉算法的性能，并支持旨在利用大量标注数据的研究，例如用于训练深度神经网络。

规模与特征
- 包含总计 33,500 个图像样本。
- 280 个语义组隶属于 15 个超类别。
- 高质量掩码标注。
- 拥有多重前景对象的多样化视觉环境。