@inproceedings{hegazy-ragab-2026-nu,
title = "{NU}{\_}{H}allucinators at {N}akba{A}rchive{C}lassifier Shared Task: A {CLIP}-Based Approach for Destruction Detection in Historical Image Archives",
author = "Hegazy, Salma Khaled and
Ragab, Mohamed Ibrahim",
editor = "Jarrar, Mustafa and
El-Haj, Mo and
Haddad, Amal and
Atiani, Serin and
Abudalfa, Shadi and
Regier, Terry and
Rayson, Paul and
Sima{'}an, Khalil and
Mansour, Camille",
booktitle = "Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.nakbanlp-1.45/",
doi = "10.63317/4c5nkazsww8o",
pages = "289--293",
abstract = "This paper presents a CLIP-based transfer learning approach for classifying historical archive images in the Nakba Image Classification Shared Task at the Nakba-NLP 2026 Workshop (LREC 2026). The task involves distinguishing images depicting destroyed or damaged infrastructure from those showing intact scenes using a dataset of 2,001 images collected from Instagram posts published by Palestinian content creators and journalists in Gaza between October 2023 and December 2025. Our method employs the CLIP ViT-B/32 visual encoder with selective fine-tuning of the final transformer block and a lightweight classification head. To address class imbalance, we apply focal loss along with standard data augmentation and threshold optimization. Experimental results show that the proposed model outperforms several CNN baselines and achieves an F1-score of 0.877 on the blind test set, securing 4th place in the shared task."
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<abstract>This paper presents a CLIP-based transfer learning approach for classifying historical archive images in the Nakba Image Classification Shared Task at the Nakba-NLP 2026 Workshop (LREC 2026). The task involves distinguishing images depicting destroyed or damaged infrastructure from those showing intact scenes using a dataset of 2,001 images collected from Instagram posts published by Palestinian content creators and journalists in Gaza between October 2023 and December 2025. Our method employs the CLIP ViT-B/32 visual encoder with selective fine-tuning of the final transformer block and a lightweight classification head. To address class imbalance, we apply focal loss along with standard data augmentation and threshold optimization. Experimental results show that the proposed model outperforms several CNN baselines and achieves an F1-score of 0.877 on the blind test set, securing 4th place in the shared task.</abstract>
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%0 Conference Proceedings
%T NU_Hallucinators at NakbaArchiveClassifier Shared Task: A CLIP-Based Approach for Destruction Detection in Historical Image Archives
%A Hegazy, Salma Khaled
%A Ragab, Mohamed Ibrahim
%Y Jarrar, Mustafa
%Y El-Haj, Mo
%Y Haddad, Amal
%Y Atiani, Serin
%Y Abudalfa, Shadi
%Y Regier, Terry
%Y Rayson, Paul
%Y Sima’an, Khalil
%Y Mansour, Camille
%S Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F hegazy-ragab-2026-nu
%X This paper presents a CLIP-based transfer learning approach for classifying historical archive images in the Nakba Image Classification Shared Task at the Nakba-NLP 2026 Workshop (LREC 2026). The task involves distinguishing images depicting destroyed or damaged infrastructure from those showing intact scenes using a dataset of 2,001 images collected from Instagram posts published by Palestinian content creators and journalists in Gaza between October 2023 and December 2025. Our method employs the CLIP ViT-B/32 visual encoder with selective fine-tuning of the final transformer block and a lightweight classification head. To address class imbalance, we apply focal loss along with standard data augmentation and threshold optimization. Experimental results show that the proposed model outperforms several CNN baselines and achieves an F1-score of 0.877 on the blind test set, securing 4th place in the shared task.
%R 10.63317/4c5nkazsww8o
%U https://aclanthology.org/2026.nakbanlp-1.45/
%U https://doi.org/10.63317/4c5nkazsww8o
%P 289-293
Markdown (Informal)
[NU_Hallucinators at NakbaArchiveClassifier Shared Task: A CLIP-Based Approach for Destruction Detection in Historical Image Archives](https://aclanthology.org/2026.nakbanlp-1.45/) (Hegazy & Ragab, NakbaNLP 2026)
ACL