The focus of this paper is on the problem of image retrieval with attribute manipulation. Our proposed work is able to manipulate the desired attributes of the query image while maintaining its other attributes. For example, the collar attribute of the query image can be changed from round to v-neck to retrieve similar images from a large dataset. A key challenge in e-commerce is that images have multiple attributes where users would like to manipulate and it is important to estimate discriminative feature representations for each of these attributes. The proposed FashionSearchNet-v2 architecture is able to learn attribute specific representations by leveraging on its weakly-supervised localization module, which ignores the unrelated features of attributes in the feature space, thus improving the similarity learning. The network is jointly trained with the combination of attribute classification and triplet ranking loss to estimate local representations. These local representations are then merged into a single global representation based on the instructed attribute manipulation where desired images can be retrieved with a distance metric. The proposed method also provides explainability for its retrieval process to help provide additional information on the attention of the network. Experiments performed on several datasets that are rich in terms of the number of attributes show that FashionSearchNet-v2 outperforms the other state-of-the-art attribute manipulation techniques. Different than our earlier work (FashionSearchNet), we propose several improvements in the learning procedure and show that the proposed FashionSearchNet-v2 can be generalized to different domains other than fashion.

本文提出了一种基于属性操控的图像检索方法，使用FashionSearchNet-v2架构学习属性特定表示，并通过弱监督定位模块提高相似性学习，所得的本地表示基于被指导的属性操控合并为单个全局表示，在多个数据集上展示了FashionSearchNet-v2的优越性和可泛化性。

FashionSearchNet-v2: 使用特征位置定位学习属性表示，用于带属性操纵的图像检索