OPA: Object Placement Assessment Dataset

Abstract

Image composition aims to generate realistic composite image by inserting an object from one image into another background image, where the placement (e.g., location, size, occlusion) of inserted object may be unreasonable, which would significantly degrade the quality of the composite image. Although some works attempted to learn object placement to create realistic composite images, they did not focus on assessing the plausibility of object placement. In this paper, we focus on object placement assessment task, which verifies whether a composite image is plausible in terms of the object placement. To accomplish this task, we construct the first Object Placement Assessment (OPA) dataset consisting of composite images and their rationality labels. We also propose a simple yet effective baseline for this task. Dataset is available at https://github.com/bcmi/Object-Placement-Assessment-Dataset-OPA.

References in corpus (5)

• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Data Augmentation for Object Detection via Progressive and Selective Instance-Switching
• Making Images Real Again: A Comprehensive Survey on Deep Image Composition
• Deep Image Harmonization
• Learning to Generate Synthetic Data via Compositing