SignEval 2026 Challenge Results

Ahmed Hasanaath, Raffaele Mineo, Hamzah Luqman, Sarah Alyami, Maad Alowaifeer, Amelia Sorrenti, Gaia Caligiore, Sabina Fontana, Egidio Ragonese, Giovanni Bellitto, Federica Proietto Salanitri, Concetto Spampinato, Motaz Alfarraj, Mufti Mahmud, Simone Palazzo, Nour Imane Zeghib; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2026, pp. 10490-10501

Abstract

This paper summarizes the results of the second multimodal sign language recognition challenge, SignEval 2026, organized at CVPR 2026. The challenge featured two tracks: (i) Continuous sign language recognition (CSLR) task based on the newly curated Isharah dataset, a Saudi Sign Language dataset, and (ii) Isolated sign language recognition (ISLR) task using the MultiMeDaLIS dataset, a multimodal Italian Sign Language corpus tailored for doctor-patient communication. The challenge utilized two leaderboards to showcase methods, with participants setting new benchmarks and achieving state-of-the-art results on both tracks. More information on the challenges, tasks, leaderboard, baselines and development kits are available on https://m-slrt.github.io/MSLR2026/

Related Material

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[bibtex]
@InProceedings{Hasanaath_2026_CVPR, author = {Hasanaath, Ahmed and Mineo, Raffaele and Luqman, Hamzah and Alyami, Sarah and Alowaifeer, Maad and Sorrenti, Amelia and Caligiore, Gaia and Fontana, Sabina and Ragonese, Egidio and Bellitto, Giovanni and Salanitri, Federica Proietto and Spampinato, Concetto and Alfarraj, Motaz and Mahmud, Mufti and Palazzo, Simone and Zeghib, Nour Imane}, title = {SignEval 2026 Challenge Results}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2026}, pages = {10490-10501} }