.png){kind=icon}
.png){kind=icon}
Return
Modality-Oriented Graph Learning Toward Outfit Compatibility Modeling
.png){kind=icon}
PRE
AI
DOI：10.1109/TMM.2021.3134164.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Outfit compatibility modeling, which aims to automatically evaluate the matching degree of an outfit, has drawn great research attention. Regarding the comprehensive evaluation, several previous studies have attempted to solve the task of outfit compatibility modeling by integrating the multi-modal information of fashion items. However, these methods primarily focus on fusing the visual and textual modalities, but seldom consider the category modality as an essential modality. In addition, they mainly focus on the exploration of the intra-modal compatibility relation among fashion items in an outfit but ignore the importance of the inter-modal compatibility relation, i.e., the compatibility across different modalities between fashion items. Since each modality of the item could deliver the same characteristics of the item as other modalities, as well as certain exclusive features of the item, overlooking the inter-modal compatibility could yield sub-optimal performance. To address these issues, a multi-modal outfit compatibility modeling scheme with modality-oriented graph learning is proposed, dubbed as MOCM-MGL, which takes both the visual, textual, and category modalities as input and jointly propagates the intra-modal and inter-modal compatibilities among fashion items. Experimental results on the real-world Polyvore Outfits-ND and Polyvore Outfits-D datasets have demonstrated the superiority of our proposed model over existing methods.
Keywords:
Visualization
Feature extraction
Estimation
Predictive models
Footwear
Task analysis
Shape
Graph convolutional network
multi-modal recommendation
outfit compatibility modeling
Journal
IF:
9.7
Papers:
4.5K
Citations:
2.4W
View more organizations