no code implementations • 23 May 2024 • Shezheng Song, Shasha Li, Shan Zhao, Chengyu Wang, Xiaopeng Li, Jie Yu, Qian Wan, Jun Ma, Tianwei Yan, Wentao Ma, Xiaoguang Mao
In contrast, a pipeline framework first identifies aspects through MATE (Multimodal Aspect Term Extraction) and then aligns these aspects with image patches for sentiment classification (MASC: Multimodal Aspect-Oriented Sentiment Classification).
Aspect-Based Sentiment Analysis Multimodal Sentiment Analysis +2
1 code implementation • 7 Apr 2024 • Shezheng Song, Shasha Li, Shan Zhao, Xiaopeng Li, Chengyu Wang, Jie Yu, Jun Ma, Tianwei Yan, Bin Ji, Xiaoguang Mao
Multimodal entity linking (MEL) aims to utilize multimodal information (usually textual and visual information) to link ambiguous mentions to unambiguous entities in knowledge base.
1 code implementation • 19 Dec 2023 • Shezheng Song, Shan Zhao, Chengyu Wang, Tianwei Yan, Shasha Li, Xiaoguang Mao, Meng Wang
Multimodal Entity Linking (MEL) aims at linking ambiguous mentions with multimodal information to entity in Knowledge Graph (KG) such as Wikipedia, which plays a key role in many applications.
no code implementations • 10 Nov 2023 • Shezheng Song, Xiaopeng Li, Shasha Li, Shan Zhao, Jie Yu, Jun Ma, Xiaoguang Mao, Weimin Zhang
The study surveys existing modal alignment methods in MLLMs into four groups: (1) Multimodal Converters that change data into something LLMs can understand; (2) Multimodal Perceivers to improve how LLMs perceive different types of data; (3) Tools Assistance for changing data into one common format, usually text; and (4) Data-Driven methods that teach LLMs to understand specific types of data in a dataset.
no code implementations • 19 Dec 2022 • Kangchen Zhu, Zhiliang Tian, Ruifeng Luo, Xiaoguang Mao
Since cycle construction helps to improve the style transfer ability of the model by rebuilding transferred sentences back to original-style sentences, it brings about a content loss in unsupervised text style transfer tasks.
no code implementations • 13 Dec 2018 • Shangwen Wang, Ming Wen, Xiaoguang Mao, Deheng Yang
Our findings show that: 1) Mockito bugs are not more complex for repairing compared with bugs from other projects; 2) the bugs repaired by the state-of-the-art tools share the same repair patterns compared with those patterns required to repair Mockito bugs; however, 3) the state-of-the-art tools perform poorly on Mockito bugs (Nopol can only correctly fix one bug while SimFix and CapGen cannot fix any bug in Mockito even if all the buggy locations have been exposed).
Software Engineering