论文标题
基于上下文的图像段标签（CBISL）
Context-based Image Segment Labeling (CBISL)
论文作者
论文摘要
使用图像，通常会面临不完整或不清信息的问题。图像介绍可用于恢复缺失的图像区域，但是将重点放在低级图像特征上，例如像素强度，像素梯度方向和颜色。本文旨在恢复图像中的语义图像特征（对象和位置）。基于已发布的封闭式PixelCNN，我们演示了一种新方法，称为Quadro方向PixelCNN，以恢复缺失的对象并根据上下文返回对象的可能位置。我们称此方法为基于上下文的图像段标签（CBISL）。结果表明，我们的四个方向模型的表现优于一个方向模型（门控PixelCNN），并返回可相机的性能。
Working with images, one often faces problems with incomplete or unclear information. Image inpainting can be used to restore missing image regions but focuses, however, on low-level image features such as pixel intensity, pixel gradient orientation, and color. This paper aims to recover semantic image features (objects and positions) in images. Based on published gated PixelCNNs, we demonstrate a new approach referred to as quadro-directional PixelCNN to recover missing objects and return probable positions for objects based on the context. We call this approach context-based image segment labeling (CBISL). The results suggest that our four-directional model outperforms one-directional models (gated PixelCNN) and returns a human-comparable performance.
