COCO-Stuff 164k Dataset

The authors of the COCO-Stuff 164k dataset discuss the significance of semantic classes, which can be categorized as either thing classes (objects with well-defined shapes, e.g., car, person) or stuff classes (amorphous background regions, e.g., grass, sky). They note that while much attention has been given to thing classes in classification and detection works, stuff classes have received less focus. However, they emphasize that stuff classes play a crucial role in understanding images, providing information about scene type, the likely presence and location of thing classes through contextual reasoning, physical attributes, material types, and geometric properties of the scene.

COCO-Stuff 164k 数据集的作者们探讨了语义类别的意义，这些类别可分为两类：一类是物体类别（具有明确形状的物体，例如汽车、人物），另一类是物质类别（非晶质背景区域，例如草地、天空）。他们指出，尽管在分类和检测工作中，物体类别受到了广泛关注，但物质类别却较少受到关注。然而，他们强调，物质类别在理解图像中扮演着至关重要的角色，它通过情境推理、物理属性、材料类型和场景的几何特性，为场景类型、物体类别的可能存在和位置提供了信息。