Dual Semantic Fusion Hashing for Multi-Label Cross-Modal Retrieval
Abstract
We propose self-organizing and simplifying neuro-fuzzy networks (NFNs) to yield transparent human-readable policies by exploiting fuzzy information granulation and graph theory. Deriving from social network analysis, we retain only the frequent-yet-discernible (FYD) patterns in NFNs and apply them to reward-based scenarios. The effectiveness of NFNs from FYD patterns is shown in classic control and a real-world classroom using an intelligent tutoring system to teach students.
Cite
Text
Liu et al. "Dual Semantic Fusion Hashing for Multi-Label Cross-Modal Retrieval." International Joint Conference on Artificial Intelligence, 2024. doi:10.24963/ijcai.2024/505Markdown
[Liu et al. "Dual Semantic Fusion Hashing for Multi-Label Cross-Modal Retrieval." International Joint Conference on Artificial Intelligence, 2024.](https://mlanthology.org/ijcai/2024/liu2024ijcai-dual/) doi:10.24963/ijcai.2024/505BibTeX
@inproceedings{liu2024ijcai-dual,
title = {{Dual Semantic Fusion Hashing for Multi-Label Cross-Modal Retrieval}},
author = {Liu, Kaiming and Gong, Yunhong and Cao, Yu and Ren, Zhenwen and Peng, Dezhong and Sun, Yuan},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2024},
pages = {4569-4577},
doi = {10.24963/ijcai.2024/505},
url = {https://mlanthology.org/ijcai/2024/liu2024ijcai-dual/}
}