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Diversity in Fashion Recommendation Using Semantic Parsing

Abstract: 

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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Paper Details

Authors:
Sukhad Anand, Chetan Arora, Atul Rai
Submitted On:
8 October 2018 - 4:26am
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Sagar Verma
Paper Code:
2499
Document Year:
2018
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Fashion recommendation based on contextual similarity

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[1] Sukhad Anand, Chetan Arora, Atul Rai, "Diversity in Fashion Recommendation Using Semantic Parsing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3622. Accessed: Dec. 10, 2018.
@article{3622-18,
url = {http://sigport.org/3622},
author = {Sukhad Anand; Chetan Arora; Atul Rai },
publisher = {IEEE SigPort},
title = {Diversity in Fashion Recommendation Using Semantic Parsing},
year = {2018} }
TY - EJOUR
T1 - Diversity in Fashion Recommendation Using Semantic Parsing
AU - Sukhad Anand; Chetan Arora; Atul Rai
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3622
ER -
Sukhad Anand, Chetan Arora, Atul Rai. (2018). Diversity in Fashion Recommendation Using Semantic Parsing. IEEE SigPort. http://sigport.org/3622
Sukhad Anand, Chetan Arora, Atul Rai, 2018. Diversity in Fashion Recommendation Using Semantic Parsing. Available at: http://sigport.org/3622.
Sukhad Anand, Chetan Arora, Atul Rai. (2018). "Diversity in Fashion Recommendation Using Semantic Parsing." Web.
1. Sukhad Anand, Chetan Arora, Atul Rai. Diversity in Fashion Recommendation Using Semantic Parsing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3622