arXiv：cs.AI· Pushp Garg, Himangshu Sarma, Mrinmoy Ghorai·· 7 小时前
混合运动学 GCN 与弹性 Transformer 实现高效印度手语识别
A Hybrid Kinematic GCN and Elastic Transformer for Efficient Indian Sign Language Recognition
AI 导读
研究者提出一种结构感知混合框架，用于印度手语（ISL）的 gloss 识别，在自建的 120 类、9,453 段视频原生 ISL 数据集上达到 89.8% 准确率。该框架用熵驱动 SVD 采样器动态提取关键帧，并以连续双位置编码配合距离感知弹性 Transformer 保留手语节奏；空间上由运动学条件自适应 GCN 统一骨骼-关节特征建模双手几何。
正文
Abstract:Sign Language Recognition (SLR) presents complex challenges due to variable signing speeds and spatial intricacies. Specifically, Indian Sign Language (ISL) requires precise modeling of its inherently bimanual nature and is bottlenecked by the scarcity of large-vocabulary benchmarks and the inability of standard models to handle irregular signing velocities. To address this, a structurally-aware hybrid framework for efficient, gloss ISL recognition is proposed. To minimize computational redundancy, an entropy-driven SVD sampler that dynamically extracts informative keyframes is used. Because this subsampling creates irregular physical time gaps, the proposed architecture employs a Continuous Dual Positional Encoding paired with a distance-aware Elastic Transformer to preserve natural signing rhythm. Spatially, a Kinematically-Conditioned Adaptive GCN explicitly models bimanual geometry using unified bone-joint features. To validate our framework, a 120-class, 9,453-video native ISL dataset is created. Augmented by Physical Node Dropout and ArcFace optimization to mitigate subject-specific memorization, our framework achieves a competitive 89.8% accuracy, establishing a strong baseline for complex sign language modeling.
| Subjects: | Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.14680 [cs.AI] |
| (or arXiv:2610.14680v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14680 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Pushp Garg [view email]
[v1]
Sat, 10 Oct 2026 07:50:57 UTC (2,938 KB)
来源：arXiv：cs.AI · arxiv.org