SignLLM: Sign Languages Production Large Language Models

• URL: http://arxiv.org/abs/2405.10718v1
• Date: Fri, 17 May 2024 12:01:43 GMT
• Title: SignLLM: Sign Languages Production Large Language Models
• Authors: Sen Fang, Lei Wang, Ce Zheng, Yapeng Tian, Chen Chen,
• Abstract summary: We introduce the first comprehensive multilingual sign language dataset named Prompt2Sign. Our dataset transforms a vast array of videos into a streamlined, model-friendly format. We propose SignLLM, the first multilingual Sign Language Production model.
• Score: 33.438444361552854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce the first comprehensive multilingual sign language dataset named Prompt2Sign, which builds from public data including American Sign Language (ASL) and seven others. Our dataset transforms a vast array of videos into a streamlined, model-friendly format, optimized for training with translation models like seq2seq and text2text. Building on this new dataset, we propose SignLLM, the first multilingual Sign Language Production (SLP) model, which includes two novel multilingual SLP modes that allow for the generation of sign language gestures from input text or prompt. Both of the modes can use a new loss and a module based on reinforcement learning, which accelerates the training by enhancing the model's capability to autonomously sample high-quality data. We present benchmark results of SignLLM, which demonstrate that our model achieves state-of-the-art performance on SLP tasks across eight sign languages.

Related papers

• T2S-GPT: Dynamic Vector Quantization for Autoregressive Sign Language Production from Text [59.57676466961787]
  We propose a novel dynamic vector quantization (DVA-VAE) model that can adjust the encoding length based on the information density in sign language. Experiments conducted on the PHOENIX14T dataset demonstrate the effectiveness of our proposed method. We propose a new large German sign language dataset, PHOENIX-News, which contains 486 hours of sign language videos, audio, and transcription texts.
  arXiv  Detail & Related papers  (2024-06-11T10:06:53Z)
• 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)
• SignDiff: Diffusion Models for American Sign Language Production [23.82668888574089]
  We propose a dual-condition diffusion pre-training model named SignDiff that can generate human sign language speakers from a skeleton pose. We also propose a new method for American Sign Language Production (ASLP), which can generate ASL skeletal pose videos from text input.
  arXiv  Detail & Related papers  (2023-08-30T15:14:56Z)
• Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
  We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
  arXiv  Detail & Related papers  (2023-06-13T08:08:08Z)
• Generalizing Multimodal Pre-training into Multilingual via Language Acquisition [54.69707237195554]
  English-based Vision-Language Pre-training has achieved great success in various downstream tasks. Some efforts have been taken to generalize this success to non-English languages through Multilingual Vision-Language Pre-training. We propose a textbfMultitextbfLingual textbfAcquisition (MLA) framework that can easily generalize a monolingual Vision-Language Pre-training model into multilingual.
  arXiv  Detail & Related papers  (2022-05-29T08:53:22Z)
• 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)
• SLM: Learning a Discourse Language Representation with Sentence Unshuffling [53.42814722621715]
  We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation. We show that this feature of our model improves the performance of the original BERT by large margins.
  arXiv  Detail & Related papers  (2020-10-30T13:33:41Z)

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.