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        <title>Pose-Based Sign Language Spotting via an End-to-End Encoder Architecture</title>
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    <abstract>Automatic Sign Language Recognition (ASLR) has emerged as a vital field for bridging the gap between deaf and hearing communities. However, the problem of sign-to-sign retrieval or detecting a specific sign within a sequence of continuous signs remains largely unexplored. We define this novel task as Sign Language Spotting. In this paper, we present a first step toward sign language retrieval by addressing the challenge of detecting the presence or absence of a query sign video within a sentence-level gloss or sign video. Unlike conventional approaches that rely on intermediate gloss recognition or text-based matching, we propose an end-to-end model that directly operates on pose keypoints extracted from sign videos. Our architecture employs an encoder-only backbone with a binary classification head to determine whether the query sign appears within the target sequence. By focusing on pose representations instead of raw RGB frames, our method significantly reduces computational cost and mitigates visual noise. We evaluate our approach on the Word Presence Prediction dataset from the WSLP 2025 shared task, achieving 61.88% accuracy and 60.00% F1-score. These results demonstrate the effectiveness of our pose-based framework for Sign Language Spotting, establishing a strong foundation for future research in automatic sign language retrieval and verification.</abstract>
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%0 Conference Proceedings
%T Pose-Based Sign Language Spotting via an End-to-End Encoder Architecture
%A Johnny, Samuel Ebimobowei
%A Guda, Blessed
%A Aaron, Emmanuel
%A Gueye, Assane
%Y Hasanuzzaman, Mohammed
%Y Quiroga, Facundo Manuel
%Y Modi, Ashutosh
%Y Kamila, Sabyasachi
%Y Artiaga, Keren
%Y Joshi, Abhinav
%Y Singh, Sanjeet
%S Proceedings of the Workshop on Sign Language Processing (WSLP)
%D 2025
%8 December
%I Association for Computational Linguistics
%C IIT Bombay, Mumbai, India (Co-located with IJCNLP–AACL 2025)
%@ 979-8-89176-304-3
%F johnny-etal-2025-pose
%X Automatic Sign Language Recognition (ASLR) has emerged as a vital field for bridging the gap between deaf and hearing communities. However, the problem of sign-to-sign retrieval or detecting a specific sign within a sequence of continuous signs remains largely unexplored. We define this novel task as Sign Language Spotting. In this paper, we present a first step toward sign language retrieval by addressing the challenge of detecting the presence or absence of a query sign video within a sentence-level gloss or sign video. Unlike conventional approaches that rely on intermediate gloss recognition or text-based matching, we propose an end-to-end model that directly operates on pose keypoints extracted from sign videos. Our architecture employs an encoder-only backbone with a binary classification head to determine whether the query sign appears within the target sequence. By focusing on pose representations instead of raw RGB frames, our method significantly reduces computational cost and mitigates visual noise. We evaluate our approach on the Word Presence Prediction dataset from the WSLP 2025 shared task, achieving 61.88% accuracy and 60.00% F1-score. These results demonstrate the effectiveness of our pose-based framework for Sign Language Spotting, establishing a strong foundation for future research in automatic sign language retrieval and verification.
%U https://aclanthology.org/2025.wslp-main.10/
%P 67-72

Markdown (Informal)

[Pose-Based Sign Language Spotting via an End-to-End Encoder Architecture](https://aclanthology.org/2025.wslp-main.10/) (Johnny et al., WSLP 2025)

• Pose-Based Sign Language Spotting via an End-to-End Encoder Architecture (Johnny et al., WSLP 2025)

ACL

• Samuel Ebimobowei Johnny, Blessed Guda, Emmanuel Aaron, and Assane Gueye. 2025. Pose-Based Sign Language Spotting via an End-to-End Encoder Architecture. In Proceedings of the Workshop on Sign Language Processing (WSLP), pages 67–72, IIT Bombay, Mumbai, India (Co-located with IJCNLP–AACL 2025). Association for Computational Linguistics.