Multimodal misinformation detection by learning from synthetic data with multimodal LLMs
F Zeng, W Li, W Gao, Y Pang - arxiv preprint arxiv:2409.19656, 2024 - arxiv.org
Detecting multimodal misinformation, especially in the form of image-text pairs, is crucial.
Obtaining large-scale, high-quality real-world fact-checking datasets for training detectors is …
Obtaining large-scale, high-quality real-world fact-checking datasets for training detectors is …
STORM: Strategic Orchestration of Modalities for Rare Event Classification
In domains such as biomedical, expert insights are crucial for selecting the most informative
modalities for artificial intelligence (AI) methodologies. However, using all available …
modalities for artificial intelligence (AI) methodologies. However, using all available …
[HTML][HTML] Regulating Modality Utilization within Multimodal Fusion Networks
Multimodal fusion networks play a pivotal role in leveraging diverse sources of information
for enhanced machine learning applications in aerial imagery. However, current approaches …
for enhanced machine learning applications in aerial imagery. However, current approaches …
A Progressive Skip Reasoning Fusion Method for Multi-Modal Classification
Q Guo, X Liang, Y Qian, Z Cui, J Wen - Proceedings of the 32nd ACM …, 2024 - dl.acm.org
In multi-modal classification tasks, a good fusion algorithm can effectively integrate and
process multi-modal data, thereby significantly improving its performance. Researchers …
process multi-modal data, thereby significantly improving its performance. Researchers …
A wrapper method for finding optimal subset of multimodal Magnetic Resonance Imaging sequences for ischemic stroke lesion segmentation
Multimodal data, while being information-rich, contains complementary as well as redundant
information. Depending on the target problem some modalities are more informative and …
information. Depending on the target problem some modalities are more informative and …