论文标题
使用深度学习的中波红外图像的多次视图生成和分类
Multiple View Generation and Classification of Mid-wave Infrared Images using Deep Learning
论文作者
论文摘要
我们提出了一项新的研究，以在非线性特征子空间中为红外图像产生不看到的任意观点。当前方法使用合成图像，通常会导致模糊和失真的输出。相反，我们的方法了解自然图像中的语义信息，并将其封装，使我们预测的看不见的观点具有良好的3D表示。我们进一步探讨了非线性特征子空间，并得出结论，我们的网络不在欧几里得子空间中运行，而是在Riemannian子空间中运行。它没有学习用于预测像素在新图像中的位置的几何变换，而是学习了歧管。为此，我们使用T-SNE可视化来对我们的网络进行详细的分析，并将生成图像的分类作为一项低射击学习任务。
We propose a novel study of generating unseen arbitrary viewpoints for infrared imagery in the non-linear feature subspace . Current methods use synthetic images and often result in blurry and distorted outputs. Our approach on the contrary understands the semantic information in natural images and encapsulates it such that our predicted unseen views possess good 3D representations. We further explore the non-linear feature subspace and conclude that our network does not operate in the Euclidean subspace but rather in the Riemannian subspace. It does not learn the geometric transformation for predicting the position of the pixel in the new image but rather learns the manifold. To this end, we use t-SNE visualisations to conduct a detailed analysis of our network and perform classification of generated images as a low-shot learning task.
