@inproceedings{boulanouar-2026-zahira,
title = "{ZAHIRA} {BOULANOUAR} at {N}akba{A}rchive{C}lassifier Shared Task: Detecting Infrastructure Destruction in {G}aza with a {C}onv{N}e{X}t Ensemble",
author = "Boulanouar, Zahira",
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-1.46/",
doi = "10.63317/2x7rcigppzib",
pages = "294--297",
abstract = "We present our third-place submission to the Nakba Image Classification Shared Task at LREC-COLING 2026, which requires binary classification of Instagram images from Gaza into destruction (damaged or destroyed infrastructure) versus not{\_}destruction. Our system fine-tunes a ConvNeXt-Tiny backbone within a five-fold stratified cross-validation framework, combining Focal Loss, weighted random sampling, exponential moving average (EMA) weight stabilization, test-time augmentation (TTA), and out-of-fold (OOF) decision threshold calibration. Our system achieves an official test macro F1 of 0.8893 and 90.05{\%} accuracy, placing third among all participants and within 0.02 F1 of the winning system (0.91), demonstrating that a 28M-parameter convolutional architecture with principled training strategies is highly competitive with much larger models."
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<abstract>We present our third-place submission to the Nakba Image Classification Shared Task at LREC-COLING 2026, which requires binary classification of Instagram images from Gaza into destruction (damaged or destroyed infrastructure) versus not_destruction. Our system fine-tunes a ConvNeXt-Tiny backbone within a five-fold stratified cross-validation framework, combining Focal Loss, weighted random sampling, exponential moving average (EMA) weight stabilization, test-time augmentation (TTA), and out-of-fold (OOF) decision threshold calibration. Our system achieves an official test macro F1 of 0.8893 and 90.05% accuracy, placing third among all participants and within 0.02 F1 of the winning system (0.91), demonstrating that a 28M-parameter convolutional architecture with principled training strategies is highly competitive with much larger models.</abstract>
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%0 Conference Proceedings
%T ZAHIRA BOULANOUAR at NakbaArchiveClassifier Shared Task: Detecting Infrastructure Destruction in Gaza with a ConvNeXt Ensemble
%A Boulanouar, Zahira
%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 boulanouar-2026-zahira
%X We present our third-place submission to the Nakba Image Classification Shared Task at LREC-COLING 2026, which requires binary classification of Instagram images from Gaza into destruction (damaged or destroyed infrastructure) versus not_destruction. Our system fine-tunes a ConvNeXt-Tiny backbone within a five-fold stratified cross-validation framework, combining Focal Loss, weighted random sampling, exponential moving average (EMA) weight stabilization, test-time augmentation (TTA), and out-of-fold (OOF) decision threshold calibration. Our system achieves an official test macro F1 of 0.8893 and 90.05% accuracy, placing third among all participants and within 0.02 F1 of the winning system (0.91), demonstrating that a 28M-parameter convolutional architecture with principled training strategies is highly competitive with much larger models.
%R 10.63317/2x7rcigppzib
%U https://aclanthology.org/2026.nakbanlp-1.46/
%U https://doi.org/10.63317/2x7rcigppzib
%P 294-297
Markdown (Informal)
[ZAHIRA BOULANOUAR at NakbaArchiveClassifier Shared Task: Detecting Infrastructure Destruction in Gaza with a ConvNeXt Ensemble](https://aclanthology.org/2026.nakbanlp-1.46/) (Boulanouar, NakbaNLP 2026)
ACL