Abstract
Intelligent fashion outfit composition has become more popular in recent years. Some deep-learning-based approaches reveal competitive composition. However, the uninterpretable characteristic makes such a deep-learning-based approach fail to meet the businesses', designers', and consumers' urges to comprehend the importance of different attributes in an outfit composition. To realize interpretable and intelligent multi-item fashion outfit compositions, we propose a partitioned embedding network to learn interpretable embeddings from clothing items. The network contains two vital components: attribute partition module and partition adversarial module. In the attribute partition module, multiple attribute labels are adopted to ensure that different parts of the overall embedding correspond to different attributes. In the partition adversarial module, adversarial operations are adopted to achieve the independence of different parts. With the interpretable and partitioned embedding, we then construct an outfit-composition graph and an attribute matching map. Extensive experiments demonstrate that (1) the partitioned embedding have unmingled parts that correspond to different attributes and (2) outfits recommended by our model are more desirable in comparison with the existing methods.
| Original language | English (US) |
|---|---|
| Article number | 61 |
| Journal | ACM Transactions on Multimedia Computing, Communications and Applications |
| Volume | 15 |
| Issue number | 2s |
| DOIs | |
| State | Published - Aug 2019 |
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Keywords
- Adversarial
- Embedding
- Interpretable
- Outfit composition
ASJC Scopus subject areas
- Hardware and Architecture
- Computer Networks and Communications
Cite this
Interpretable partitioned embedding for intelligent multi-item fashion outfit composition. / Feng, Zunlei; Yu, Zhenyun; Jing, Yongcheng; Wu, Sai; Song, Mingli; Yang, Yezhou; Jiang, Junxiao.
In: ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 15, No. 2s, 61, 08.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Interpretable partitioned embedding for intelligent multi-item fashion outfit composition
AU - Feng, Zunlei
AU - Yu, Zhenyun
AU - Jing, Yongcheng
AU - Wu, Sai
AU - Song, Mingli
AU - Yang, Yezhou
AU - Jiang, Junxiao
PY - 2019/8
Y1 - 2019/8
N2 - Intelligent fashion outfit composition has become more popular in recent years. Some deep-learning-based approaches reveal competitive composition. However, the uninterpretable characteristic makes such a deep-learning-based approach fail to meet the businesses', designers', and consumers' urges to comprehend the importance of different attributes in an outfit composition. To realize interpretable and intelligent multi-item fashion outfit compositions, we propose a partitioned embedding network to learn interpretable embeddings from clothing items. The network contains two vital components: attribute partition module and partition adversarial module. In the attribute partition module, multiple attribute labels are adopted to ensure that different parts of the overall embedding correspond to different attributes. In the partition adversarial module, adversarial operations are adopted to achieve the independence of different parts. With the interpretable and partitioned embedding, we then construct an outfit-composition graph and an attribute matching map. Extensive experiments demonstrate that (1) the partitioned embedding have unmingled parts that correspond to different attributes and (2) outfits recommended by our model are more desirable in comparison with the existing methods.
AB - Intelligent fashion outfit composition has become more popular in recent years. Some deep-learning-based approaches reveal competitive composition. However, the uninterpretable characteristic makes such a deep-learning-based approach fail to meet the businesses', designers', and consumers' urges to comprehend the importance of different attributes in an outfit composition. To realize interpretable and intelligent multi-item fashion outfit compositions, we propose a partitioned embedding network to learn interpretable embeddings from clothing items. The network contains two vital components: attribute partition module and partition adversarial module. In the attribute partition module, multiple attribute labels are adopted to ensure that different parts of the overall embedding correspond to different attributes. In the partition adversarial module, adversarial operations are adopted to achieve the independence of different parts. With the interpretable and partitioned embedding, we then construct an outfit-composition graph and an attribute matching map. Extensive experiments demonstrate that (1) the partitioned embedding have unmingled parts that correspond to different attributes and (2) outfits recommended by our model are more desirable in comparison with the existing methods.
KW - Adversarial
KW - Embedding
KW - Interpretable
KW - Outfit composition
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UR - http://www.scopus.com/inward/citedby.url?scp=85071081381&partnerID=8YFLogxK
U2 - 10.1145/3326332
DO - 10.1145/3326332
M3 - Article
AN - SCOPUS:85071081381
VL - 15
JO - ACM Transactions on Multimedia Computing, Communications and Applications
JF - ACM Transactions on Multimedia Computing, Communications and Applications
SN - 1551-6857
IS - 2s
M1 - 61
ER -