An Efficient Sign Language Translation Using Spatial Configuration and Motion Dynamics with LLMs

• URL: http://arxiv.org/abs/2408.10593v1
• Date: Tue, 20 Aug 2024 07:10:40 GMT
• Title: An Efficient Sign Language Translation Using Spatial Configuration and Motion Dynamics with LLMs
• Authors: Eui Jun Hwang, Sukmin Cho, Junmyeong Lee, Jong C. Park,
• Abstract summary: Gloss-free Sign Language Translation (SLT) converts sign videos directly into spoken language sentences without relying on glosses. This paper emphasizes the importance of capturing the spatial configurations and motion dynamics inherent in sign language. We introduce Spatial and Motion-based Sign Language Translation (SpaMo), a novel LLM-based SLT framework.
• Score: 7.630967411418269
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gloss-free Sign Language Translation (SLT) converts sign videos directly into spoken language sentences without relying on glosses. Recently, Large Language Models (LLMs) have shown remarkable translation performance in gloss-free methods by harnessing their powerful natural language generation capabilities. However, these methods often rely on domain-specific fine-tuning of visual encoders to achieve optimal results. By contrast, this paper emphasizes the importance of capturing the spatial configurations and motion dynamics inherent in sign language. With this in mind, we introduce Spatial and Motion-based Sign Language Translation (SpaMo), a novel LLM-based SLT framework. The core idea of SpaMo is simple yet effective. We first extract spatial and motion features using off-the-shelf visual encoders and then input these features into an LLM with a language prompt. Additionally, we employ a visual-text alignment process as a warm-up before the SLT supervision. Our experiments demonstrate that SpaMo achieves state-of-the-art performance on two popular datasets, PHOENIX14T and How2Sign.

Related papers

• EvSign: Sign Language Recognition and Translation with Streaming Events [59.51655336911345]
  Event camera could naturally perceive dynamic hand movements, providing rich manual clues for sign language tasks. We propose efficient transformer-based framework for event-based SLR and SLT tasks. Our method performs favorably against existing state-of-the-art approaches with only 0.34% computational cost.
  arXiv  Detail & Related papers  (2024-07-17T14:16:35Z)
• LLMs are Good Sign Language Translators [19.259163728870696]
  Sign Language Translation is a challenging task that aims to translate sign videos into spoken language. We propose a novel SignLLM framework to transform sign videos into a language-like representation. We achieve state-of-the-art gloss-free results on two widely-used SLT benchmarks.
  arXiv  Detail & Related papers  (2024-04-01T05:07:13Z)
• Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language Translation [28.648494997132925]
  We propose Factorized Learning assisted with Large Language Model (FLa-LLM) for gloss-free Sign Language Translation (SLT) We factorize the training process into two stages. In the visual initialing stage, we employ a lightweight translation model after the visual encoder to pre-train the visual encoder. In the LLM fine-tuning stage, we freeze the acquired knowledge in the visual encoder and integrate it with a pre-trained LLM to inspire the LLM's translation potential.
  arXiv  Detail & Related papers  (2024-03-19T09:00:23Z)
• Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization [52.935150075484074]
  We introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language. The resulting visual tokens encompass high-level semantics worthy of a word and also support dynamic sequence length varying from the image. This unification empowers LaVIT to serve as an impressive generalist interface to understand and generate multi-modal content simultaneously.
  arXiv  Detail & Related papers  (2023-09-09T03:01:38Z)
• 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)
• Gloss Attention for Gloss-free Sign Language Translation [60.633146518820325]
  We show how gloss annotations make sign language translation easier. We then propose emphgloss attention, which enables the model to keep its attention within video segments that have the same semantics locally. Experimental results on multiple large-scale sign language datasets show that our proposed GASLT model significantly outperforms existing methods.
  arXiv  Detail & Related papers  (2023-07-14T14:07:55Z)
• A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation [54.29679610921429]
  Existing sign language datasets contain only about 10K-20K pairs of sign videos, gloss annotations and texts. Data is thus a bottleneck for training effective sign language translation models. This simple baseline surpasses the previous state-of-the-art results on two sign language translation benchmarks.
  arXiv  Detail & Related papers  (2022-03-08T18:59:56Z)
• SimulSLT: End-to-End Simultaneous Sign Language Translation [55.54237194555432]
  Existing sign language translation methods need to read all the videos before starting the translation. We propose SimulSLT, the first end-to-end simultaneous sign language translation model. SimulSLT achieves BLEU scores that exceed the latest end-to-end non-simultaneous sign language translation model.
  arXiv  Detail & Related papers  (2021-12-08T11:04:52Z)

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.