Matching User Photos to Online Products with Robust Deep Features

Wang, Xi; Sun, Zhenfeng; Zhang, Wenqiang; Zhou, Yu; Jiang, Yu‐Gang

doi:10.1145/2911996.2912002

Cited by 54 publications

(41 citation statements)

References 19 publications

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“…finding similarity between two images using Siamese network [1,2,3]. In an extension of the work, it is also possible to learn similarity-dissimilarity between three images using a Triplet network [4,5].…”

Section: Figmentioning

confidence: 99%

Diversity in Fashion Recommendation Using Semantic Parsing

Verma

Anand

Arora

et al. 2018

2018 25th IEEE International Conference on Image Processing (ICIP)

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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.

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Section: Figmentioning

confidence: 99%

Diversity in Fashion Recommendation Using Semantic Parsing

Verma

Anand

Arora

et al. 2018

2018 25th IEEE International Conference on Image Processing (ICIP)

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“…Cross-scenario clothing retrieval has widely applicability for commercial systems. There have been extensive efforts on similar clothing retrieval [1,7,8,16,13,17] and exactly same clothing retrieval [14,23].…”

Section: Cross-scenario Clothing Retrievalmentioning

confidence: 99%

“…For exactly same clothing retrieval, exact matching street clothing photos in online shops is firstly explored in [14]. A robust deep feature representation is learned in [23] to bridge the domain gap between the street and shops. A new deep model, namely FashionNet, is proposed in [18], which learns clothing features by jointly predicting clothing attributes and land-marks.…”

Section: Cross-scenario Clothing Retrievalmentioning

confidence: 99%

“…Although the street-to-shop clothing matching problem, which searches the online clothing by street fashion photos, has been explored recently [7,8,14,23], finding clothes appeared in videos to the exact same items in online shops is not well studied yet. The diverse appearance of cloth, cluttered scenes, occlusion, different light condition and motion blur in the video make video2shop challenging.…”

Section: Introductionmentioning

confidence: 99%

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Video2Shop: Exact Matching Clothes in Videos to Online Shopping Images

Cheng

Liu

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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In recent years, both online retail and video hosting service are exponentially growing. In this paper, we explore a new cross-domain task, Video2Shop, targeting for matching clothes appeared in videos to the exact same items in online shops. A novel deep neural network, called AsymNet, is proposed to explore this problem. For the image side, wellestablished methods are used to detect and extract features for clothing patches with arbitrary sizes. For the video side, deep visual features are extracted from detected object regions in each frame, and further fed into a Long Short-Term Memory (LSTM) framework for sequence modeling, which captures the temporal dynamics in videos. To conduct exact matching between videos and online shopping images, LSTM hidden states, representing the video, and image features, which represent static object images, are jointly modeled under the similarity network with reconfigurable deep tree structure. Moreover, an approximate training method is proposed to achieve the efficiency when training. Extensive experiments conducted on a large cross-domain dataset have demonstrated the effectiveness and efficiency of the proposed AsymNet, which outperforms the state-of-the-art methods.

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“…Computer vision for fashion e-commerce images has drawn an increasing interest in the last decade. It has been used for similarity search [20,11,21,18], automatic image tagging [10,2], fine-grained classification [5,12] or N-shot learning [1]. In all of these tasks, a model's performance is highly dependent on a visual feature extractor.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction

Corbière¹,

Ben-younes

Ramé³

et al. 2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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In this paper, we present a method to learn a visual representation adapted for e-commerce products. Based on weakly supervised learning, our model learns from noisy datasets crawled on e-commerce website catalogs and does not require any manual labeling. We show that our representation can be used for downward classification tasks over clothing categories with different levels of granularity. We also demonstrate that the learnt representation is suitable for image retrieval. We achieve nearly state-of-art results on the DeepFashion In-Shop Clothes Retrieval and Categories Attributes Prediction [12] tasks, without using the provided training set.

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Matching User Photos to Online Products with Robust Deep Features

Cited by 54 publications

References 19 publications

Diversity in Fashion Recommendation Using Semantic Parsing

Diversity in Fashion Recommendation Using Semantic Parsing

Video2Shop: Exact Matching Clothes in Videos to Online Shopping Images

Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction

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