HAL (France)
open access
Explainable AI for sign language recognition models: Integrating Grad-Cam LIME and Integrated Gradients
Sign language recognition is crucial in bridging the communication gaps between hearing and deaf communities. In this study, we build on an existing sign language classification model based on the VGG19 architecture, enhancing its robustness through dataset augmentation and alternative data represen
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Enhancing speech representation learning with cross-modal knowledge transfer with HGNN under low resource settings: the case study of Yemba#1048938
- Chehre: An Emoji-Prompted Dataset to Explore Perceptual Flexibility in Video Language Models#925185
- Chehre: An Emoji-Prompted Dataset to Explore Perceptual Flexibility in Video Language Models#920950
- Enhancing Human-Computer Interaction With Real-Time Hand Gesture Translation#106291
- Automated Sign Language Recognition with Machine Learning#700161
Record · ID 1203765
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive