@inproceedings{ali-etal-2026-metric,
title = "Metric-Learning Retrieval for Cross-Signer Isolated Sign Language Recognition",
author = "Ali, Md. Asif and
Apurbo, Md. Mosharaf Hossain and
Hasan, Kazi Reyazul",
editor = "Singh, Sanjeet and
Joshi, Abhinav and
Dattatreya, Pokala and
Artiaga, Keren and
Pujara, Khushi Jaydeep and
{\v{Z}}elezn{\'y}, Tom{\'a}{\v{s}} and
Gruber, Ivan and
Honz{\'i}k, Jakub and
Kamila, Sabyasachi and
Quiroga, Facundo Manuel and
Hr{\'u}z, Marek and
Hasanuzzaman, Mohammed and
Modi, Ashutosh",
booktitle = "Proceedings of the Second Workshop on Sign Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wslp-1.7/",
pages = "70--76",
ISBN = "979-8-89176-488-0",
abstract = "We address cross-signer Isolated Sign Language Recognition (ISLR) for Indian Sign Language as part of the WSLP-2026 shared task, where most target words have exactly one available training video and a system must recognize a query video performed by a signer never seen for that class during training. We approach this with a metric-learning encoder trained in two phases: pretraining on a large, multi-signer American Sign Language corpus to learn general signer-invariance, then fine-tuning on the target vocabulary using every real cross-signer recording we can responsibly source. Our preprocessing detects and extracts multiple performances of a word within a single recording as separate training samples, a technique not previously reported in SLR literature, and adapts a published missing-keypoint interpolation technique into a retrieval rather than classification pipeline. Our final system achieves 67.11{\%} Top-1, 69.20{\%} Top-5, and 70.34{\%} Top-10 accuracy on the WSLP-2026 test-phase evaluation."
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<abstract>We address cross-signer Isolated Sign Language Recognition (ISLR) for Indian Sign Language as part of the WSLP-2026 shared task, where most target words have exactly one available training video and a system must recognize a query video performed by a signer never seen for that class during training. We approach this with a metric-learning encoder trained in two phases: pretraining on a large, multi-signer American Sign Language corpus to learn general signer-invariance, then fine-tuning on the target vocabulary using every real cross-signer recording we can responsibly source. Our preprocessing detects and extracts multiple performances of a word within a single recording as separate training samples, a technique not previously reported in SLR literature, and adapts a published missing-keypoint interpolation technique into a retrieval rather than classification pipeline. Our final system achieves 67.11% Top-1, 69.20% Top-5, and 70.34% Top-10 accuracy on the WSLP-2026 test-phase evaluation.</abstract>
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%0 Conference Proceedings
%T Metric-Learning Retrieval for Cross-Signer Isolated Sign Language Recognition
%A Ali, Md. Asif
%A Apurbo, Md. Mosharaf Hossain
%A Hasan, Kazi Reyazul
%Y Singh, Sanjeet
%Y Joshi, Abhinav
%Y Dattatreya, Pokala
%Y Artiaga, Keren
%Y Pujara, Khushi Jaydeep
%Y Železný, Tomáš
%Y Gruber, Ivan
%Y Honzík, Jakub
%Y Kamila, Sabyasachi
%Y Quiroga, Facundo Manuel
%Y Hrúz, Marek
%Y Hasanuzzaman, Mohammed
%Y Modi, Ashutosh
%S Proceedings of the Second Workshop on Sign Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-488-0
%F ali-etal-2026-metric
%X We address cross-signer Isolated Sign Language Recognition (ISLR) for Indian Sign Language as part of the WSLP-2026 shared task, where most target words have exactly one available training video and a system must recognize a query video performed by a signer never seen for that class during training. We approach this with a metric-learning encoder trained in two phases: pretraining on a large, multi-signer American Sign Language corpus to learn general signer-invariance, then fine-tuning on the target vocabulary using every real cross-signer recording we can responsibly source. Our preprocessing detects and extracts multiple performances of a word within a single recording as separate training samples, a technique not previously reported in SLR literature, and adapts a published missing-keypoint interpolation technique into a retrieval rather than classification pipeline. Our final system achieves 67.11% Top-1, 69.20% Top-5, and 70.34% Top-10 accuracy on the WSLP-2026 test-phase evaluation.
%U https://aclanthology.org/2026.wslp-1.7/
%P 70-76
Markdown (Informal)
[Metric-Learning Retrieval for Cross-Signer Isolated Sign Language Recognition](https://aclanthology.org/2026.wslp-1.7/) (Ali et al., WSLP 2026)
ACL