AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A novel framework based on conditional Variational autoencoder for SLT (VSLT) that facilitates direct and sufficient cross-modal alignment between sign language videos and spoken language text is proposed, and a shared Attention Residual Gaussian Distribution (ARGD) which considers the textual information as a residual component relative to the prior path is proposed.
Sign Language Production (SLP) plays a crucial role in bridging the communication gap between the Deaf community and broader society, functioning alongside Sign Language Translation (SLT) and Recognition (SLR). In addition to the limited scale of available data, research on Vietnamese Sign Language (VSL) is further hin...
This work proposes a novel text-to-sign translation based on model pretraining, which enhances semantic alignment by inheriting codebook-oriented prior knowledge from masked self-supervised models.
Large Language Models (LLMs) have achieved remarkable success across a wide range of tasks. However, fine-tuning LLMs for Gloss-Free Sign Language Translation (GFSLT) remains a challenge. In this paper, we investigate how to effectively adapt LLMs to the GFSLT task. We show that there are two key issues that need to be...
We investigate the effect of pretrained T5 model scale and explicit motion features on pose-to-text Indian Sign Language Translation (SLT) for the WSLP 2026 Shared Task. Pose sequences are projected into the embedding space of T5 through a lightweight pose encoder, with the complete model fine-tuned to generate English...
To enable bidirectional mapping between text and sign, SignFLIP adopts a symmetric architecture together with a stage-wise training strategy built on large-scale data, which shows competitive performance compared with task-specific models on both translation and generation tasks, as well as strong transferability to si...
This work introduces an alternative inspired by language-learning assessment, using an open-weight-LLM QA protocol that measures salient content preservation that aligns more closely with human rankings and is six to seven times more paraphrase-invariant than BLEU-4.
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