@inproceedings{jaber-2026-pixel,
title = "Pixel at {N}akba{A}rchive{C}lassifier Shared Task: {C}onv{N}e{X}t-Based Ensemble for Destruction Detection",
author = "Jaber, Rahaf",
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-1.32/",
doi = "10.63317/3o45xv3zt4nu",
pages = "221--225",
abstract = "This paper describes our submission to the Nakba Image Classification Shared Task at the Nakba-NLP 2026 workshop. The task requires binary classification of social media images into two categories: destruction and not{\_}destruction. The dataset includes approximately 1,600 annotated development images and 400 held-out test images, collected from Instagram posts published in Gaza between October 2023 and December 2025. High variability in viewpoint, lighting, and image quality, coupled with the inherent complexities of identifying structural damage in dense urban environments, makes this task particularly challenging. Our system utilizes a pretrained ConvNeXt-Tiny backbone fine-tuned through a stratified 5-fold cross-validation framework. To mitigate class imbalance, we implement a weighted cross-entropy loss function. During the inference phase, we employ an ensemble strategy that averages predictions across all five fold-specific models, and test-time augmentation (TTA) is applied to enhance robustness. The final ensemble achieved a Macro F1-score of 0.8952 and an accuracy of 0.9055 on the official test set. Our results suggest that the integration of modern convolutional architectures with robust ensembling and augmentation strategies provides a reliable baseline for automated destruction detection."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="jaber-2026-pixel">
<titleInfo>
<title>Pixel at NakbaArchiveClassifier Shared Task: ConvNeXt-Based Ensemble for Destruction Detection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Rahaf</namePart>
<namePart type="family">Jaber</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mustafa</namePart>
<namePart type="family">Jarrar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mo</namePart>
<namePart type="family">El-Haj</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Amal</namePart>
<namePart type="family">Haddad</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Serin</namePart>
<namePart type="family">Atiani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shadi</namePart>
<namePart type="family">Abudalfa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Terry</namePart>
<namePart type="family">Regier</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Paul</namePart>
<namePart type="family">Rayson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Khalil</namePart>
<namePart type="family">Sima’an</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Camille</namePart>
<namePart type="family">Mansour</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA Language Resources Association (ELRA)</publisher>
<place>
<placeTerm type="text">Palma, Mallorca (Spain)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper describes our submission to the Nakba Image Classification Shared Task at the Nakba-NLP 2026 workshop. The task requires binary classification of social media images into two categories: destruction and not_destruction. The dataset includes approximately 1,600 annotated development images and 400 held-out test images, collected from Instagram posts published in Gaza between October 2023 and December 2025. High variability in viewpoint, lighting, and image quality, coupled with the inherent complexities of identifying structural damage in dense urban environments, makes this task particularly challenging. Our system utilizes a pretrained ConvNeXt-Tiny backbone fine-tuned through a stratified 5-fold cross-validation framework. To mitigate class imbalance, we implement a weighted cross-entropy loss function. During the inference phase, we employ an ensemble strategy that averages predictions across all five fold-specific models, and test-time augmentation (TTA) is applied to enhance robustness. The final ensemble achieved a Macro F1-score of 0.8952 and an accuracy of 0.9055 on the official test set. Our results suggest that the integration of modern convolutional architectures with robust ensembling and augmentation strategies provides a reliable baseline for automated destruction detection.</abstract>
<identifier type="citekey">jaber-2026-pixel</identifier>
<identifier type="doi">10.63317/3o45xv3zt4nu</identifier>
<location>
<url>https://aclanthology.org/2026.nakbanlp-1.32/</url>
</location>
<part>
<date>2026-05</date>
<extent unit="page">
<start>221</start>
<end>225</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Pixel at NakbaArchiveClassifier Shared Task: ConvNeXt-Based Ensemble for Destruction Detection
%A Jaber, Rahaf
%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 jaber-2026-pixel
%X This paper describes our submission to the Nakba Image Classification Shared Task at the Nakba-NLP 2026 workshop. The task requires binary classification of social media images into two categories: destruction and not_destruction. The dataset includes approximately 1,600 annotated development images and 400 held-out test images, collected from Instagram posts published in Gaza between October 2023 and December 2025. High variability in viewpoint, lighting, and image quality, coupled with the inherent complexities of identifying structural damage in dense urban environments, makes this task particularly challenging. Our system utilizes a pretrained ConvNeXt-Tiny backbone fine-tuned through a stratified 5-fold cross-validation framework. To mitigate class imbalance, we implement a weighted cross-entropy loss function. During the inference phase, we employ an ensemble strategy that averages predictions across all five fold-specific models, and test-time augmentation (TTA) is applied to enhance robustness. The final ensemble achieved a Macro F1-score of 0.8952 and an accuracy of 0.9055 on the official test set. Our results suggest that the integration of modern convolutional architectures with robust ensembling and augmentation strategies provides a reliable baseline for automated destruction detection.
%R 10.63317/3o45xv3zt4nu
%U https://aclanthology.org/2026.nakbanlp-1.32/
%U https://doi.org/10.63317/3o45xv3zt4nu
%P 221-225
Markdown (Informal)
[Pixel at NakbaArchiveClassifier Shared Task: ConvNeXt-Based Ensemble for Destruction Detection](https://aclanthology.org/2026.nakbanlp-1.32/) (Jaber, NakbaNLP 2026)
ACL