arXiv AI
Aug 10
TransSLR: A Lightweight Transformer for Sign Language Recognition
arXiv:2608. 06407v1 Announce Type: cross Abstract: Automated Sign Language Recognition for under-represented languages remains a largely unsolved problem.
Investigating Temporal Motion Features for Pose-to-Text Indian Sign Language Translation
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arXiv AI
Aug 10
SignVerse-2M: A Two-Million-Clip Pose-Native Universe of 55+ Sign Languages
arXiv:2605. 01720v3 Announce Type: replace-cross Abstract: Existing large-scale sign language resources typically provide supervision only at the level of raw video-text alignment and are often produced in laboratory settings.
Hugging Face Trending Papers
Sep 22
Isolated Sign Language Recognition for Icelandic Sign Language: Experiments in a Low-resource Setting
We present the first experiments on isolated sign language recognition (ISLR) for Icelandic Sign Language (ÍTM). We use ÍTM SignWiki, a dataset derived from a bilingual Icelandic--ÍTM online dictionar...
arXiv AI
Jul 7
ViPo-MLLM: Visual-Pose Multimodal LLM for Gloss-Free Sign Language Translation
arXiv:2607. 03657v1 Announce Type: cross Abstract: Gloss-free Sign Language Translation (SLT) translates sign language videos into spoken-language sentences without gloss annotations, avoiding costly labeling but requiring fine-grained modeling of hands, body, and facial cues.
arXiv Computation and Language
Sep 23
Isolated Sign Language Recognition for Icelandic Sign Language: Experiments in a Low-resource Setting
arXiv:2609.25862v1 Announce Type: new Abstract: We present the first experiments on isolated sign language recognition (ISLR) for Icelandic Sign Language (\'ITM). We use \'ITM SignWiki, a dataset der...
arXiv AI
Jul 7
A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition
arXiv:2204. 02803v2 Announce Type: replace-cross Abstract: Sign language recognition from monocular video or 2D pose sequences is challenging, both because 3D information must be inferred from 2D observations and because the signal is inherently spatiotemporal.
arXiv Computer Vision
Sep 18
Attention-Steered Vision-Language Models for Sign Language Translation
The paper introduces AttnSign, a vision‑language model that improves sign language translation by steering spatial‑temporal attention. It first supervises attention on sign‑relevant regions such as faces and hands in each frame, then uses an RL‑based motion‑cadence method to focus on keyframes. Experiments on How2Sign and OpenASL show AttnSign consistently outperforms existing methods.
arXiv Computer Vision
Sep 15
SignMimic: Robust High-Quality Sign Language Motion Generation via Human-Shape-Oblivious Pose Transfer Guidance
arXiv:2609.14122v1 Announce Type: new Abstract: We study the challenge of sign language video mimicking: given a driving video and a single reference frame, synthesize a video where the target signer...
arXiv Computer Vision
Aug 27
SMART: MLLM-guided Temporal Alignment for Unifying Sign Language Recognition and Spotting
SMART is a new framework that jointly tackles continuous sign language recognition (CSLR) and spotting by leveraging a multimodal large language model (MLLM) to generate motion descriptions as auxiliary semantic cues. It performs stable video‑text alignment with small batch sizes and introduces a Multi‑Scale Temporal Adapter to capture temporal interactions during transformer encoding. The framework also incorporates CSFormer, a CSLR‑guided spotting module that injects recognition‑derived gloss evidence into a boundary‑aware spotting network, enabling mutual benefit between recognition and spotting tasks.
arXiv Computer Vision
Sep 21
SignGPT: Toward LLM-Mediated Sign Language Interaction through Gloss-Free Translation and Generation
SignGPT is a unified, pose‑based framework that performs gloss‑free sign language translation (SLT) and generation (SLG) by integrating part‑aware hierarchical representations of body, hand, and facial motion into a shared language model. It uses asymmetric multi‑token prediction and progressive training for bidirectional modeling, and is evaluated on How2Sign (ASL) and Phoenix‑2014T (DGS) with benchmark comparisons, qualitative analyses, and component ablations. An exploratory study with 12 Deaf ASL signers demonstrates a sign‑to‑sign response pipeline, suggesting that unified modeling can support sign language conversation (SLC).
arXiv AI
Oct 15
A Hybrid Kinematic GCN and Elastic Transformer for Efficient Indian Sign Language Recognition
arXiv:2610.14680v1 Announce Type: new Abstract: Sign Language Recognition (SLR) presents complex challenges due to variable signing speeds and spatial intricacies. Specifically, Indian Sign Language...
arXiv AI
Sep 4
Beyond BLEU: A Case for Redefining Sign Language Translation Benchmarks
The paper argues that BLEU-4, the prevailing metric for sign language translation (SLT), may not accurately reflect sign language proficiency because SLT models can exploit spurious correlations and spoken-language priors. By evaluating six SLT models on Phoenix-2014T and CSL-Daily, the authors show that higher BLEU-4 scores do not necessarily indicate better spatio-temporal understanding. They propose a new open-weight LLM QA protocol inspired by language-learning assessment, which better preserves salient content, aligns more closely with human rankings, and reveals differences between gloss-free and gloss-supervised systems that BLEU-4 obscures.