Spotter+GPT: Turning Sign Spottings into Sentences with LLMs
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912531622133760 |
|---|---|
| author | Sincan, Ozge Mercanoglu Bowden, Richard |
| author_facet | Sincan, Ozge Mercanoglu Bowden, Richard |
| contents | Sign Language Translation (SLT) is a challenging task that aims to generate spoken language sentences from sign language videos. In this paper, we introduce a lightweight, modular SLT framework, Spotter+GPT, that leverages the power of Large Language Models (LLMs) and avoids heavy end-to-end training. Spotter+GPT breaks down the SLT task into two distinct stages. First, a sign spotter identifies individual signs within the input video. The spotted signs are then passed to an LLM, which transforms them into meaningful spoken language sentences. Spotter+GPT eliminates the requirement for SLT-specific training. This significantly reduces computational costs and time requirements. The source code and pretrained weights of the Spotter are available at https://gitlab.surrey.ac.uk/cogvispublic/sign-spotter. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10434 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spotter+GPT: Turning Sign Spottings into Sentences with LLMs Sincan, Ozge Mercanoglu Bowden, Richard Computer Vision and Pattern Recognition Sign Language Translation (SLT) is a challenging task that aims to generate spoken language sentences from sign language videos. In this paper, we introduce a lightweight, modular SLT framework, Spotter+GPT, that leverages the power of Large Language Models (LLMs) and avoids heavy end-to-end training. Spotter+GPT breaks down the SLT task into two distinct stages. First, a sign spotter identifies individual signs within the input video. The spotted signs are then passed to an LLM, which transforms them into meaningful spoken language sentences. Spotter+GPT eliminates the requirement for SLT-specific training. This significantly reduces computational costs and time requirements. The source code and pretrained weights of the Spotter are available at https://gitlab.surrey.ac.uk/cogvispublic/sign-spotter. |
| title | Spotter+GPT: Turning Sign Spottings into Sentences with LLMs |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.10434 |