SCOPE: Sign Language Contextual Processing with Embedding from LLMs

• URL: http://arxiv.org/abs/2409.01073v1
• Date: Mon, 2 Sep 2024 08:56:12 GMT
• Title: SCOPE: Sign Language Contextual Processing with Embedding from LLMs
• Authors: Yuqi Liu, Wenqian Zhang, Sihan Ren, Chengyu Huang, Jingyi Yu, Lan Xu,
• Abstract summary: Sign languages, used by around 70 million Deaf individuals globally, are visual languages that convey visual and contextual information. Current methods in vision-based sign language recognition ( SLR) and translation (SLT) struggle with dialogue scenes due to limited dataset diversity and the neglect of contextually relevant information. We introduce SCOPE, a novel context-aware vision-based SLR and SLT framework.
• Score: 49.5629738637893
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Sign languages, used by around 70 million Deaf individuals globally, are visual languages that convey visual and contextual information. Current methods in vision-based sign language recognition (SLR) and translation (SLT) struggle with dialogue scenes due to limited dataset diversity and the neglect of contextually relevant information. To address these challenges, we introduce SCOPE (Sign language Contextual Processing with Embedding from LLMs), a novel context-aware vision-based SLR and SLT framework. For SLR, we utilize dialogue contexts through a multi-modal encoder to enhance gloss-level recognition. For subsequent SLT, we further fine-tune a Large Language Model (LLM) by incorporating prior conversational context. We also contribute a new sign language dataset that contains 72 hours of Chinese sign language videos in contextual dialogues across various scenarios. Experimental results demonstrate that our SCOPE framework achieves state-of-the-art performance on multiple datasets, including Phoenix-2014T, CSL-Daily, and our SCOPE dataset. Moreover, surveys conducted with participants from the Deaf community further validate the robustness and effectiveness of our approach in real-world applications. Both our dataset and code will be open-sourced to facilitate further research.

Related papers

• Enhancing Multilingual Speech Generation and Recognition Abilities in LLMs with Constructed Code-switched Data [30.966072545451183]
  We propose a MutltiLingual MultiTask (MLMT) model, integrating multilingual speech generation and recognition tasks within the single LLM. We develop an effective data construction approach that splits and equips words from different languages to equip synthesiss with CS ability without relying on CS data.
  arXiv  Detail & Related papers  (2024-09-17T08:11:07Z)
• Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via Adapters [3.7273829129985305]
  This paper explores integration of graph knowledge from linguistic into multilingual Large Models (LLMs) We employ language-specific adapters to improve performance for low-resource languages (LRLs) in sentiment analysis (SA) and named entity recognition (NER) We assess how structured graph knowledge affects the performance of multilingual LLMs for LRLs in SA and NER.
  arXiv  Detail & Related papers  (2024-07-01T15:56:24Z)
• A Tale of Two Languages: Large-Vocabulary Continuous Sign Language Recognition from Spoken Language Supervision [74.972172804514]
  We introduce a multi-task Transformer model, CSLR2, that is able to ingest a signing sequence and output in a joint embedding space between signed language and spoken language text. New dataset annotations provide continuous sign-level annotations for six hours of test videos, and will be made publicly available. Our model significantly outperforms the previous state of the art on both tasks.
  arXiv  Detail & Related papers  (2024-05-16T17:19:06Z)
• SignVTCL: Multi-Modal Continuous Sign Language Recognition Enhanced by Visual-Textual Contrastive Learning [51.800031281177105]
  SignVTCL is a continuous sign language recognition framework enhanced by visual-textual contrastive learning. It integrates multi-modal data (video, keypoints, and optical flow) simultaneously to train a unified visual backbone. It achieves state-of-the-art results compared with previous methods.
  arXiv  Detail & Related papers  (2024-01-22T11:04:55Z)
• SSHR: Leveraging Self-supervised Hierarchical Representations for Multilingual Automatic Speech Recognition [9.853451215277346]
  We propose a novel method that leverages self-supervised hierarchical representations (SSHR) to fine-tune the MMS model. We evaluate SSHR on two multilingual datasets, Common Voice and ML-SUPERB, and the experimental results demonstrate that our method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-09-29T02:35:36Z)
• Gloss-free Sign Language Translation: Improving from Visual-Language Pretraining [56.26550923909137]
  Gloss-Free Sign Language Translation (SLT) is a challenging task due to its cross-domain nature. We propose a novel Gloss-Free SLT based on Visual-Language Pretraining (GFSLT-) Our approach involves two stages: (i) integrating Contrastive Language-Image Pre-training with masked self-supervised learning to create pre-tasks that bridge the semantic gap between visual and textual representations and restore masked sentences, and (ii) constructing an end-to-end architecture with an encoder-decoder-like structure that inherits the parameters of the pre-trained Visual and Text Decoder from
  arXiv  Detail & Related papers  (2023-07-27T10:59:18Z)
• Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning [98.60739735409243]
  Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks. We introduce XSGD for cross-lingual alignment pretraining, a parallel and large-scale multilingual conversation dataset. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts.
  arXiv  Detail & Related papers  (2023-04-03T18:46:01Z)
• LSA-T: The first continuous Argentinian Sign Language dataset for Sign Language Translation [52.87578398308052]
  Sign language translation (SLT) is an active field of study that encompasses human-computer interaction, computer vision, natural language processing and machine learning. This paper presents the first continuous Argentinian Sign Language (LSA) dataset. It contains 14,880 sentence level videos of LSA extracted from the CN Sordos YouTube channel with labels and keypoints annotations for each signer.
  arXiv  Detail & Related papers  (2022-11-14T14:46:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.