Papers › A comparison of small sample methods for Handshape Recognition
A comparison of small sample methods for Handshape Recognition
Facundo Quiroga, Franco Ronchetti, Ulises Jeremias Cornejo Fandos, Gastón Gustavo Ríos, Pedro Dal Bianco, Waldo Hasperué, Laura Lanzarini
Automatic Sign Language Translation (SLT) systems can be a great asset to improve the communication with and within deaf communities. Currently, the main issue preventing effective translation models lays in the low availability of labelled data, which hinders the use of modern deep learning models. SLT is a complex problem that involves many subtasks, of which handshape recognition is the most important. We compare a series of models specially tailored for small datasets to improve their performance on handshape recognition tasks. We evaluate Wide-DenseNet and few-shot Prototypical Network models with and without transfer learning, and also using Model-Agnostic Meta-Learning (MAML). Our findings indicate that Wide-DenseNet without transfer learning and Prototipical Networks with transfer learning provide the best results. Prototypical networks, particularly, are vastly superior when using less than 30 samples, while Wide-DenseNet achieves the best results with more samples. On the other hand, MAML does not improve performance in any scenario. These results can help to design better SLT models.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Hand Gesture Recognition | LSA16 | Prototypical Networks + CNN | Accuracy | 98.38 | #1 of 3 | Archive leaderboard | report |
| Hand Gesture Recognition | RWTH-PHOENIX Handshapes dev set | DenseNet | Accuracy | 96.05 | #1 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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