Category-Aware Multimodal Attention Network for Fashion Compatibility Modeling-Reference-Cited by-同舟云学术

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Category-Aware Multimodal Attention Network for Fashion Compatibility Modeling

Published:2023 Issue: Volume:25 Page:9120-9131
ISSN:1520-9210
Container-title:IEEE Transactions on Multimedia
language:
Short-container-title:IEEE Trans. Multimedia

Author:

Jing Peiguang¹^ORCID,Cui Kai¹^ORCID,Guan Weili²^ORCID,Nie Liqiang³^ORCID,Su Yuting¹

Affiliation:

1. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

2. Faculty of Information Technology, Monash University (Clayton Campus), Clayton, VIC, Australia

3. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China

Funder

Guangxi Key Lab of Multi-source Information Mining & Security

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Media Technology,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6046/10016790/10049142.pdf?arnumber=10049142

Reference51 articles.

1. Mining Fashion Outfit Composition Using an End-to-End Deep Learning Approach on Set Data

2. Modality-Oriented Graph Learning Toward Outfit Compatibility Modeling

3. $A^3$-FKG: Attentive Attribute-Aware Fashion Knowledge Graph for Outfit Preference Prediction

4. Learning Similarity Conditions Without Explicit Supervision

5. Fashion Outfit Complementary Item Retrieval

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1. A deep low-rank semantic factorization method for micro-video multi-label classification;Multimedia Systems;2024-08

2. Unifying heterogeneous and homogeneous relations for personalized compatibility modeling;Knowledge-Based Systems;2024-04

3. Personalized Clothing Prediction Algorithm Based on Multi-modal Feature Fusion;International Journal of Engineering and Technology Innovation;2024-03-27

4. A long-short dual-mode knowledge distillation framework for empirical asset pricing models in digital financial networks;Connection Science;2024-01-25

5. Outfit compatibility model using fully connected self-adjusting graph neural network;The Visual Computer;2024-01-22

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