Abstract An object can be consisting of various attributes, such as illuminance, appearance, shape, orientation, etc. Separately extract these attributes has enormous value in visual effects modeling, attribute-specific retrieval and recognition. Essentially, these attributes can be fairly abstract and thus need labels to extract. However, sometimes the labels of these attributes may not be available with training data. A solution to this problem is projecting the observed data into a lower dimension latent subspace, such that each observed data can be represented by a latent variable. After that, the dimensions of a latent variable can be segmented into different parts by weighting the kernel automatic relevance determination (ARD) parameters. Consequently, the latent variable is segmented into different parts each of which corresponds to the main attribute. In real life scenery, the attributes of an object may vary significantly from case to case. For instance, a single face can probably be under different illuminance conditions. Taking into account the diversity of these attribute variations, we propose the Diversified Shared Latent Variable Model (DSLVM) to extract and manipulate object attributes in an unsupervised way. More specifically, we initially set up two views that share the same latent variables. Then, two Diversity Encouraging (DE) priors are applied to the inducing points of each model view. Here, the inducing points are a small representative dataset that explains the observed data in its entirety. Meanwhile, the exploited diversity encouraging priors are able to cover more diverse characteristics of the attributes. The defined objective function is computed by variational inference. Extensive experiments on different datasets demonstrate that our method can accurately deal with various object.

中文翻译：

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一种用于高效图像特征提取和建模的多样化共享潜变量模型

摘要 一个物体可以由各种属性组成，如照度、外观、形状、方向等，单独提取这些属性在视觉效果建模、特定属性检索和识别中具有巨大的价值。本质上，这些属性可能相当抽象，因此需要标签来提取。但是，有时这些属性的标签可能无法用于训练数据。这个问题的解决方案是将观察到的数据投影到一个较低维度的潜在子空间中，这样每个观察到的数据都可以用一个潜在变量来表示。之后，通过对内核自动相关性确定 (ARD) 参数进行加权，可以将潜在变量的维度分割成不同的部分。最后，潜在变量被分割成不同的部分，每个部分对应于主要属性。在现实生活场景中，对象的属性可能因情况而异。例如，一张脸可能处于不同的照度条件下。考虑到这些属性变化的多样性，我们提出了多元化共享潜在变量模型（DSLVM）以无监督的方式提取和操作对象属性。更具体地说，我们最初设置了两个共享相同潜在变量的视图。然后，将两个多样性鼓励 (DE) 先验应用于每个模型视图的诱导点。在这里，诱导点是一个小的代表性数据集，它完整地解释了观察到的数据。同时，所利用的多样性鼓励先验能够涵盖更多样化的属性特征。定义的目标函数是通过变分推理计算的。在不同数据集上的大量实验表明，我们的方法可以准确地处理各种对象。