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NepOOC-M: Bilingual Nepali-English Benchmark and Comparative Analysis of Multimodal Architectures for OOC Detection — arXiv:2608.19212

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.19212
License (as archived): CC0-1.0
Archived: 2026-10-18T11:39:09.333Z
SHA-256 of the archived text: 9b6105a8f3bb17ac487911945674039c0ce036f9036384dcb84c62dbf413608c
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How to cite

NepOOC-M: Bilingual Nepali-English Benchmark and Comparative Analysis of Multimodal Architectures for OOC Detection — arXiv:2608.19212 — arXiv (en), https://arxiv.org/abs/2608.19212 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bd4be186c032 (Metraipolis archive dc_bd4be186c032, sha256 9b6105a8f3bb17ac487911945674039c0ce036f9036384dcb84c62dbf413608c)

BibTeX

@misc{dcbd4be186c032,
  title = {NepOOC-M: Bilingual Nepali-English Benchmark and Comparative Analysis of Multimodal Architectures for OOC Detection — arXiv:2608.19212},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.19212},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bd4be186c032; sha256 9b6105a8f3bb17ac487911945674039c0ce036f9036384dcb84c62dbf413608c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.19212. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Out-of-context (OOC) misinformation pairs authentic images with misleading captions to construct false narratives without image manipulation, making detection a problem of multimodal alignment rather than image forensics. Despite the prevalence and consequences of OOC misinformation in Nepal, no public benchmark exists for Nepali. We introduce NepOOC, the first publicly available Nepali-dominant multilingual OOC benchmark, comprising 1,090 image-caption pairs (545 pristine, 545 OOC) annotated across five typologies (fabricated, miscaptioned, temporal mismatch, geographic mismatch, identity mismatch) with inter-annotator agreement kappa = 0.84. Systematic evaluation of five multimodal architectures alongside text-only and image-only baselines reveals that caption semantics appear sufficient for strong performance at the current dataset scale. A text-only mBERT model achieves 94.65+/-0.20% Macro-F1, statistically equivalent to the best multimodal system (ResNet-50+mBERT, 94.65+/-0.20%; McNemar median p = 1.000, 0/5 seeds significant at alpha = 0.05). Image-only models perform near chance (33-50%), while training-size scaling suggests that dataset expansion is a more direct path to progress than architectural sophistication or regional specialisation.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-21 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.19212 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T11:38:27.886Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-08-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).