Matching clothing images from customers and online shopping stores has rich
applications in E-commerce. Existing algorithms encoded an image as a global
feature vector and performed retrieval with the global representation. However,
discriminative local information on clothes are submerged in this global
representation, resulting in sub-optimal performance. To address this issue, we
propose a novel Graph Reasoning Network (GRNet) on a Similarity Pyramid, which
learns similarities between a query and a gallery cloth by using both global
and local representations in multiple scales. The similarity pyramid is
represented by a Graph of similarity, where nodes represent similarities
between clothing components at different scales, and the final matching score
is obtained by message passing along edges. In GRNet, graph reasoning is solved
by training a graph convolutional network, enabling to align salient clothing
components to improve clothing retrieval. To facilitate future researches, we
introduce a new benchmark FindFashion, containing rich annotations of bounding
boxes, views, occlusions, and cropping. Extensive experiments show that GRNet
obtains new state-of-the-art results on two challenging benchmarks, e.g.,
pushing the top-1, top-20, and top-50 accuracies on DeepFashion to 26%, 64%,
and 75% (i.e., 4%, 10%, and 10% absolute improvements), outperforming
competitors with large margins. On FindFashion, GRNet achieves considerable
improvements on all empirical settings.

该研究提出了一种新颖的基于相似性金字塔的图推理网络（GRNet）算法，通过多尺度的全局和局部表征学习查询和画廊衣服的相似性，以解决现有算法中将判别的局部信息淹没在全局表征中的不足，进而实现了服装图像在电子商务中的精准检索。

基于相似性金字塔的图推理网络对时尚检索

Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid

In this study, we investigate in-shop clothing retrieval performance of
densely-connected Capsule Networks with dynamic routing. To achieve this, we
propose Triplet-based design of Capsule Network architecture with two different
feature extraction methods. In our design, Stacked-convolutional (SC) and
Residual-connected (RC) blocks are used to form the input of capsule layers.
Experimental results show that both of our designs outperform all variants of
the baseline study, namely FashionNet, without relying on the landmark
information. Moreover, when compared to the SOTA architectures on clothing
retrieval, our proposed Triplet Capsule Networks achieve comparable recall
rates only with half of parameters used in the SOTA architectures.

本研究提出了一种基于三元组的胶囊网络结构，并采用叠加卷积和残差连接块作为胶囊层的输入，在无需地标信息的情况下，取得了比基准研究 FashionNet 更好的性能，在服装检索任务上的表现也与最先进的模型相当，但所使用的参数数量仅为后者的一半。

胶囊网络在时尚图像检索中的应用

Fashion Image Retrieval with Capsule Networks

Clothing retrieval is a challenging problem in computer vision. With the
advance of Convolutional Neural Networks (CNNs), the accuracy of clothing
retrieval has been significantly improved. FashionNet[1], a recent study,
proposes to employ a set of artificial features in the form of landmarks for
clothing retrieval, which are shown to be helpful for retrieval. However, the
landmark detection module is trained with strong supervision which requires
considerable efforts to obtain. In this paper, we propose a self-learning
Visual Attention Model (VAM) to extract attention maps from clothing images.
The VAM is further connected to a global network to form an end-to-end network
structure through Impdrop connection which randomly Dropout on the feature maps
with the probabilities given by the attention map. Extensive experiments on
several widely used benchmark clothing retrieval data sets have demonstrated
the promise of the proposed method. We also show that compared to the trivial
Product connection, the Impdrop connection makes the network structure more
robust when training sets of limited size are used.

利用卷积神经网络进行图像检索是一个具有挑战性的问题，该研究提出了一个自学习的视觉注意模型以及一个 End-to-End 网络结构，通过嵌入 Impdrop 模块进一步优化，取得了优异的检索性能。