The paper introduces the Sequential Spatio-Temporal Attention Network (SSTAN), a Transformer-based architecture that replaces traditional Graph Convolutional Networks for sign language recognition. SSTAN uses a hierarchical, stacked design with Spatial Multi-Head Attention to model joint relationships within frames and Temporal Multi-Head Attention to capture long-range dependencies across frames, eliminating the need for predefined skeletal graphs. Experiments on large-scale datasets (WLASL, JSL, KSL) show that SSTAN, trained from scratch, achieves state‑of‑the‑art performance in fingerspelling categories and outperforms other skeleton‑only methods on WLASL, highlighting its data efficiency and ability to learn complex spatio‑temporal patterns.
By Koki Hirooka, Abu Saleh Musa Miah, Tatsuya Murakami, Md. Al Mehedi Hasan, Yong Seok Hwang, Jungpil Shin
arXiv:2608. 06407v1 Announce Type: cross Abstract: Automated Sign Language Recognition for under-represented languages remains a largely unsolved problem.
By Lucia Yen Wanchi, Samuel Johnny, Victor Tolulope Olufemi, Emmanuel Aaron, Moise Busogi
arXiv:2608. 06252v1 Announce Type: cross Abstract: Deaf and hard-of-hearing people in Bangladesh communicate mainly through Bangla Sign Language (BdSL).
By Saad Ahmed, Md Khalid Syfullaha
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.
By Silvan Ferreira, Esdras Costa, Marcio Dahia, Jampierre Rocha
arXiv:2607. 22779v1 Announce Type: cross Abstract: Hand gesture recognition via surface electromyography (sEMG) is fundamental to prosthetic control.
By Federico Del Pup, Elisa Tentori, Manfredo Atzori
arXiv:2608. 09400v1 Announce Type: cross Abstract: Research regarding the sign language recognition mostly relies on RGB images, whileas sign language datasets that provide depth images are limited.
By Rustem Ozakar, Eyup Gedikli
arXiv:2609.18772v1 Announce Type: new
Abstract: Sign language processing advances rapidly for high-resource languages such as American Sign Language (ASL), yet most of the world's sign languages lack...
By Marcel Granero-Moya, Carolina del Corral Farrar\'os, Gloria Haro, Coloma Ballester, Ricardo Marques
SignBind-LLM introduces a modular framework for sign language translation that separates continuous signing, fingerspelling, and lipreading into dedicated expert streams. Each expert is pre‑trained independently on about two million pseudo‑gloss sequences, eliminating the need for manual gloss annotation. A lightweight transformer fuses the expert outputs, and a pre‑trained language model converts the fused pseudo‑glosses into fluent English, achieving state‑of‑the‑art performance on multiple benchmarks with lower training cost.
By Marshall Thomas, Edward Fish, Richard Bowden
arXiv:2608. 13368v1 Announce Type: cross Abstract: This preliminary technical report presents a framework for sign language video synthesis using a loss-guided multi-expert Generative Adversarial Network (GAN) to enhance communication for individuals with hearing impairments.
By Dingzhan Nong, Zhihao Ren, Ziqi Li, Tim Lo
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.
By Ahmed Abul Hasanaath, Bicheng Xu, Mir Rayat Imtiaz Hossain, Leonid Sigal, Hamzah Luqman
arXiv:2608. 16804v1 Announce Type: new Abstract: Sign language serves as a vital means of communication for individuals with hearing impairments, yet recognition resources for the over 100 distinct sign languages are severely lacking.
By Keren Artiaga (Victor), Yang Li (Victor), Ercan Engin Kuruoglu (Victor), Wai Kin (Victor), Chan
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.
By Eunjee Choi, JungHoon Sung, Seongwhan Cho, Chu Xin, Younggeun Choi