12 research outputs found
Diversity in Fashion Recommendation using Semantic Parsing
Developing recommendation system for fashion images is challenging due to the
inherent ambiguity associated with what criterion a user is looking at.
Suggesting multiple images where each output image is similar to the query
image on the basis of a different feature or part is one way to mitigate the
problem. Existing works for fashion recommendation have used Siamese or Triplet
network to learn features between a similar pair and a similar-dissimilar
triplet respectively. However, these methods do not provide basic information
such as, how two clothing images are similar, or which parts present in the two
images make them similar. In this paper, we propose to recommend images by
explicitly learning and exploiting part based similarity. We propose a novel
approach of learning discriminative features from weakly-supervised data by
using visual attention over the parts and a texture encoding network. We show
that the learned features surpass the state-of-the-art in retrieval task on
DeepFashion dataset. We then use the proposed model to recommend fashion images
having an explicit variation with respect to similarity of any of the parts.Comment: 5 pages, ICIP2018, code:
https://github.com/sagarverma/fashion_recommendation_stlst
Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network
This paper strives to learn fine-grained fashion similarity. In this
similarity paradigm, one should pay more attention to the similarity in terms
of a specific design/attribute among fashion items, which has potential values
in many fashion related applications such as fashion copyright protection. To
this end, we propose an Attribute-Specific Embedding Network (ASEN) to jointly
learn multiple attribute-specific embeddings in an end-to-end manner, thus
measure the fine-grained similarity in the corresponding space. With two
attention modules, i.e., Attribute-aware Spatial Attention and Attribute-aware
Channel Attention, ASEN is able to locate the related regions and capture the
essential patterns under the guidance of the specified attribute, thus make the
learned attribute-specific embeddings better reflect the fine-grained
similarity. Extensive experiments on four fashion-related datasets show the
effectiveness of ASEN for fine-grained fashion similarity learning and its
potential for fashion reranking.Comment: 16 pages, 13 figutes. Accepted by AAAI 2020. Code and data are
available at https://github.com/Maryeon/ase
Deep Learning with Discriminative Margin Loss for Cross-Domain Consumer-to-Shop Clothes Retrieval
Consumer-to-shop clothes retrieval refers to the problem of matching photos taken by customers with their counterparts in the shop. Due to some problems, such as a large number of clothing categories, different appearances of clothing items due to different camera angles and shooting conditions, different background environments, and different body postures, the retrieval accuracy of traditional consumer-to-shop models is always low. With advances in convolutional neural networks (CNNs), the accuracy of garment retrieval has been significantly improved. Most approaches addressing this problem use single CNNs in conjunction with a softmax loss function to extract discriminative features. In the fashion domain, negative pairs can have small or large visual differences that make it difficult to minimize intraclass variance and maximize interclass variance with softmax. Margin-based softmax losses such as Additive Margin-Softmax (aka CosFace) improve the discriminative power of the original softmax loss, but since they consider the same margin for the positive and negative pairs, they are not suitable for cross-domain fashion search. In this work, we introduce the cross-domain discriminative margin loss (DML) to deal with the large variability of negative pairs in fashion. DML learns two different margins for positive and negative pairs such that the negative margin is larger than the positive margin, which provides stronger intraclass reduction for negative pairs. The experiments conducted on publicly available fashion datasets DARN and two benchmarks of the DeepFashion dataset—(1) Consumer-to-Shop Clothes Retrieval and (2) InShop Clothes Retrieval—confirm that the proposed loss function not only outperforms the existing loss functions but also achieves the best performance