IJCAI 2022poster3 citations
Visual Emotion Representation Learning via Emotion-Aware Pre-training
Yue Zhang, Wanying Ding, Ran Xu, Xiaohua Hu
Abstract
Despite recent progress in deep learning, visual emotion recognition remains a challenging problem due to ambiguity of emotion perception, diverse concepts related to visual emotion and lack of large-scale annotated dataset. In this paper, we present a large-scale multimodal pre-training method to learn visual emotion representation by aligning emotion, object, attribute triplet with a contrastive loss. We conduct our pre-training on a large web dataset with noisy tags and fine-tune on visual emotion classification datasets. Our method achieves state-of-the-art performance for visual emotion classification.
Computer Vision: Vision and languageNatural Language Processing: Sentiment Analysis and Text MiningHumans and AI: Cognitive Modeling
BibTeX
@inproceedings{ijcai2022p234,
title = {Visual Emotion Representation Learning via Emotion-Aware Pre-training},
author = {Zhang, Yue and Ding, Wanying and Xu, Ran and Hu, Xiaohua},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {1679--1685},
year = {2022},
month = {7},
note = {Main Track},
doi = {10.24963/ijcai.2022/234},
url = {https://doi.org/10.24963/ijcai.2022/234},
}