AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The proposed LCASP-MMANet framework provides an effective balance between accuracy, robustness, and computational efficiency for sign language recognition, and competitive results on the VOC benchmark demonstrate strong generalization capability.
SST-Net is proposed, a spatiotemporal collaborative attention enhancement framework for SLR that introduces input-dependent gates to dynamically fuse spatial and channel attention, rather than using fixed fusion weights.
Context. The fundamental need for Sign Language Recognition (SLR) arises from the persistent communication gap between the deaf/hard-of-hearing community and the non-signing public. Traditional SLR models, however, are often hindered by the high computational complexity and overhead associated with modeling the tempora...
This proposed research presents a real-time American Sign Language (ASL) recognition system for 24 dynamic signs that integrates the MediaPipe frame work with Long Short-Term Memory (LSTM) network to achieve efficient real-time dynamic sign recognition with reduced computational and data requirements.
An AI-driven real-time Swahili Sign Language Recognition system designed to bridge communication gaps between the hearing-impaired community and the general population and outperforming several existing models is proposed.
Sign Language Recognition (SLR) has gained increasing attention, while Continuous Sign Language Recognition (CSLR) remains substantially more challenging than Isolated SLR (ISLR) due to the difficulty of accurately detecting sign boundaries within continuous signing streams. This challenge is intensified by co-articula...
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