Papers with SOTA approach
MetaSumPerceiver: Multimodal Multi-Document Evidence Summarization for Fact-Checking (2024.acl-long)
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| Challenge: | Fact-checking claims on social media platforms poses a significant challenge due to the large volume of new claims constantly being posted without sufficient methods for verification. |
| Approach: | They propose a model that generates claim-specific summaries from multimodal multi-document datasets using a perceiver-based model that is able to handle inputs from multiple modalities of arbitrary lengths. |
| Outcome: | The proposed model outperforms the SOTA approach by 4.6% in the claim verification task on the MOCHEG dataset and shows strong performance on the new multi-document claims dataset. |
HomoGraphAdapter: A Homogeneous Graph Neural Network as an Effective Adapter for Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Existing adaptation methods overlook structural knowledge between text and image modalities or create overly complex graphs containing redundant information for alignment. |
| Approach: | They propose a method to adapt visual models to downstream tasks using text and image modalities. |
| Outcome: | The proposed method improves classification accuracy by 1.51% for 1-shot and 0.74% for 16-shot on 11 datasets. |