Papers by Gabriel Thiem
COVE: COntext and VEracity prediction for out-of-context images (2025.naacl-long)
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| Challenge: | Existing automated fact-checking methods fail to tackle both objectives explicitly. |
| Approach: | They propose a method that predicts first the true COntext of the image and then uses it to predict the VEracity of the caption. |
| Outcome: | The proposed method beats the SOTA context prediction model on all context items, often by more than five percentage points, and is reusable and interpretable to verify new out-of-context captions for the same image. |
MONETA: Multimodal Industry Classification through Geographic Information with Multi Agent Systems (2026.acl-long)
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| Challenge: | MONETA is the first multimodal industry classification benchmark with text and geospatial sources. |
| Approach: | They propose a multimodal industry classification benchmark using text and geospatial sources. |
| Outcome: | The proposed model increases the accuracy of the existing models by 22.80%. |