no code implementations • 24 Apr 2024 • Weizhi Zhang, Liangwei Yang, Zihe Song, Henry Peng Zou, Ke Xu, Yuanjie Zhu, Philip S. Yu
Graph contrastive learning aims to learn from high-order collaborative filtering signals with unsupervised augmentation on the user-item bipartite graph, which predominantly relies on the multi-task learning framework involving both the pair-wise recommendation loss and the contrastive loss.
1 code implementation • 24 Apr 2024 • Henry Peng Zou, Vinay Samuel, Yue Zhou, Weizhi Zhang, Liancheng Fang, Zihe Song, Philip S. Yu, Cornelia Caragea
To address these limitations, we present ImplicitAVE, the first, publicly available multimodal dataset for implicit attribute value extraction.
no code implementations • 13 Apr 2024 • Henry Peng Zou, Gavin Heqing Yu, Ziwei Fan, Dan Bu, Han Liu, Peng Dai, Dongmei Jia, Cornelia Caragea
To address these issues, we introduce EIVEN, a data- and parameter-efficient generative framework that pioneers the use of multimodal LLM for implicit attribute value extraction.
1 code implementation • 23 Oct 2023 • Henry Peng Zou, Yue Zhou, Cornelia Caragea, Doina Caragea
The shared real-time information about natural disasters on social media platforms like Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and response organizations.
1 code implementation • 23 Oct 2023 • Henry Peng Zou, Cornelia Caragea
However, existing approaches based on pseudo-labeling suffer from the issues of pseudo-label bias and error accumulation.
1 code implementation • 23 Oct 2023 • Henry Peng Zou, Yue Zhou, Weizhi Zhang, Cornelia Caragea
During crisis events, people often use social media platforms such as Twitter to disseminate information about the situation, warnings, advice, and support.