@inproceedings{le-2026-kvochurhegel-nakbaarchiveclassifier,
title = "{K}vochur{H}egel at {N}akba{A}rchive{C}lassifier Shared Task: Nakba Image Classification via {C}onv{N}e{X}t-V2 and Label Smoothing",
author = "Le, Minh-Hoang",
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-1.14/",
doi = "10.63317/4uecz9j2m37s",
pages = "118--120",
abstract = "This paper presents the KvochurHegel team{'}s submission to the Nakba Image Classification shared task at the Nakba-NLP 2026 Workshop. The task requires the binary classification of social media images into destruction and not{\_}destruction categories. Given a limited and imbalanced training set of 1,400 images, we utilized a ConvNeXt-V2 Nano backbone combined with extensive data augmentation and label smoothing, prioritizing standard regularization over task-specific architectural modifications. For inference, we applied a 6-view Test-Time Augmentation (TTA) strategy using a hard-voting mechanism. The baseline system achieved a Macro F1-score of 0.8593 and an Accuracy of 0.8706 on the official private test set, ranking 6th out of 16 participating teams."
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<abstract>This paper presents the KvochurHegel team’s submission to the Nakba Image Classification shared task at the Nakba-NLP 2026 Workshop. The task requires the binary classification of social media images into destruction and not_destruction categories. Given a limited and imbalanced training set of 1,400 images, we utilized a ConvNeXt-V2 Nano backbone combined with extensive data augmentation and label smoothing, prioritizing standard regularization over task-specific architectural modifications. For inference, we applied a 6-view Test-Time Augmentation (TTA) strategy using a hard-voting mechanism. The baseline system achieved a Macro F1-score of 0.8593 and an Accuracy of 0.8706 on the official private test set, ranking 6th out of 16 participating teams.</abstract>
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%0 Conference Proceedings
%T KvochurHegel at NakbaArchiveClassifier Shared Task: Nakba Image Classification via ConvNeXt-V2 and Label Smoothing
%A Le, Minh-Hoang
%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 le-2026-kvochurhegel-nakbaarchiveclassifier
%X This paper presents the KvochurHegel team’s submission to the Nakba Image Classification shared task at the Nakba-NLP 2026 Workshop. The task requires the binary classification of social media images into destruction and not_destruction categories. Given a limited and imbalanced training set of 1,400 images, we utilized a ConvNeXt-V2 Nano backbone combined with extensive data augmentation and label smoothing, prioritizing standard regularization over task-specific architectural modifications. For inference, we applied a 6-view Test-Time Augmentation (TTA) strategy using a hard-voting mechanism. The baseline system achieved a Macro F1-score of 0.8593 and an Accuracy of 0.8706 on the official private test set, ranking 6th out of 16 participating teams.
%R 10.63317/4uecz9j2m37s
%U https://aclanthology.org/2026.nakbanlp-1.14/
%U https://doi.org/10.63317/4uecz9j2m37s
%P 118-120
Markdown (Informal)
[KvochurHegel at NakbaArchiveClassifier Shared Task: Nakba Image Classification via ConvNeXt-V2 and Label Smoothing](https://aclanthology.org/2026.nakbanlp-1.14/) (Le, NakbaNLP 2026)
ACL