Papers with multimodal fusion modules

2 papers
RethinkingTMSC: An Empirical Study for Target-Oriented Multimodal Sentiment Classification (2023.findings-emnlp)

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Challenge: Recent studies have shown that current TMSC systems rely on textual information, and the progress in tackling this task has slowed down.
Approach: They propose to integrate both visual and textual information to improve the performance of TMSC by considering multimodal information.
Outcome: The proposed model integrates both visual and textual information to improve performance.
Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding (2024.findings-acl)

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Challenge: Recent research in large vision-language models has shown promising results, but the issue of hallucination remains.
Approach: They propose an instruction-based method to reduce hallucinations in large vision-language models . they use disturbance instructions to exacerbate hallucinosity in multimodal fusion modules .
Outcome: The proposed method reduces hallucinations in multimodal fusion modules by reducing alignment uncertainty and subtracting hallucines from the original distribution.

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