论文标题
快速对象放置评估
Fast Object Placement Assessment
论文作者
论文摘要
对象放置评估（OPA）旨在根据插入的前景对象的放置（例如比例，位置）来预测复合图像的合理性评分。但是，给定一对缩放的前景和背景，为了列举所有合理的位置，现有的OPA模型需要将前景放置在背景上的每个位置，并一次通过模型一个通过模型将所获得的复合图像传递，这非常耗时。在这项工作中，我们研究了一个名为Fast OPA的新任务。具体而言，我们只有一个缩放的前景和背景，我们只能通过模型将它们通过一次，并预测所有位置的理性得分。为了完成这项任务，我们提出了一个开创性的快速OPA模型，该模型具有多种创新（即前景动态过滤器，背景先验传输和模仿复合功能），以弥合慢速OPA模型和快速OPA模型之间的性能差距。 OPA数据集上的广泛实验表明，我们提出的快速OPA模型以慢速OPA模型的速度表现出色，但运行速度明显更快。
Object placement assessment (OPA) aims to predict the rationality score of a composite image in terms of the placement (e.g., scale, location) of inserted foreground object. However, given a pair of scaled foreground and background, to enumerate all the reasonable locations, existing OPA model needs to place the foreground at each location on the background and pass the obtained composite image through the model one at a time, which is very time-consuming. In this work, we investigate a new task named as fast OPA. Specifically, provided with a scaled foreground and a background, we only pass them through the model once and predict the rationality scores for all locations. To accomplish this task, we propose a pioneering fast OPA model with several innovations (i.e., foreground dynamic filter, background prior transfer, and composite feature mimicking) to bridge the performance gap between slow OPA model and fast OPA model. Extensive experiments on OPA dataset show that our proposed fast OPA model performs on par with slow OPA model but runs significantly faster.
