The SignEval 2025 Challenge at the ICCV Multimodal Sign Language Recognition Workshop: Results and Discussion
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F25%3A43976625" target="_blank" >RIV/49777513:23520/25:43976625 - isvavai.cz</a>
Výsledek na webu
<a href="https://ieeexplore.ieee.org/document/11375246" target="_blank" >https://ieeexplore.ieee.org/document/11375246</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICCVW69036.2025.00529" target="_blank" >10.1109/ICCVW69036.2025.00529</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
The SignEval 2025 Challenge at the ICCV Multimodal Sign Language Recognition Workshop: Results and Discussion
Popis výsledku v původním jazyce
This paper summarizes the results of the first multimodal sign language recognition challenge, SignEval 2025, organized at ICCV 2025. 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. Two tasks are defined within the CSLR track: Signer-Independent and Unseen-Sentences. The Signer-Independent task tests the model's ability to generalize across signers, a critical property for scalable real-world CSLR systems. The Unseen-Sentences task evaluates the model's capability to recognize novel sentence compositions by leveraging learned grammar and semantics. The ISLR track utilized MultiMeDaLIS, a multi-modal dataset. The participants of this track were challenged to classify isolated signs using only radar and RGB modalities. 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://multimodal-sign-language-recognition.github.io/ICCV-2025/.
Název v anglickém jazyce
The SignEval 2025 Challenge at the ICCV Multimodal Sign Language Recognition Workshop: Results and Discussion
Popis výsledku anglicky
This paper summarizes the results of the first multimodal sign language recognition challenge, SignEval 2025, organized at ICCV 2025. 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. Two tasks are defined within the CSLR track: Signer-Independent and Unseen-Sentences. The Signer-Independent task tests the model's ability to generalize across signers, a critical property for scalable real-world CSLR systems. The Unseen-Sentences task evaluates the model's capability to recognize novel sentence compositions by leveraging learned grammar and semantics. The ISLR track utilized MultiMeDaLIS, a multi-modal dataset. The participants of this track were challenged to classify isolated signs using only radar and RGB modalities. 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://multimodal-sign-language-recognition.github.io/ICCV-2025/.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
20205 - Automation and control systems
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2025
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
ISBN
979-8-3315-8988-2
ISSN
2473-9936
e-ISSN
2473-9944
Počet stran výsledku
10
Strana od-do
5086-5095
Název nakladatele
Institute of Electrical and Electronics Engineers Inc.
Místo vydání
Honolulu
Místo konání akce
Honolulu, Hawai
Datum konání akce
19. 10. 2025
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—