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Found 8 papers, 0 papers with code
SignAttention: On the Interpretability of Transformer Models for Sign Language Translation
This paper presents the first comprehensive interpretability analysis of a Transformer-based Sign Language Translation (SLT) model, focusing on the translation from video-based Greek Sign Language to glosses and text.
FGR-Net:Interpretable fundus imagegradeability classification based on deepreconstruction learning
The extracted features by the autoencoder are then fed into a deep classifier network to distinguish between gradable and ungradable fundus images.
LSA64: An Argentinian Sign Language Dataset
The dataset, called LSA64, contains 3200 videos of 64 different LSA signs recorded by 10 subjects, and is a first step towards building a comprehensive research-level dataset of Argentinian signs, specifically tailored to sign language recognition or other machine learning tasks.
Handshape recognition for Argentinian Sign Language using ProbSom
Automatic sign language recognition is an important topic within the areas of human-computer interaction and machine learning.
Ranked #3 on
Hand Gesture Recognition
on LSA16
Distribution of Action Movements (DAM): A Descriptor for Human Action Recognition
Human action recognition from skeletal data is an important and active area of research in which the state of the art has not yet achieved near-perfect accuracy on many well-known datasets.
Ranked #1 on
Skeleton Based Action Recognition
on MSR Action3D
Invariance Measures for Neural Networks
We propose measures to quantify the invariance of neural networks in terms of their internal representation.
Sign Languague Recognition without frame-sequencing constraints: A proof of concept on the Argentinian Sign Language
The model employs a bag-of-words approach in all classification steps, to explore the hypothesis that ordering is not essential for recognition.
Revisiting Data Augmentation for Rotational Invariance in Convolutional Neural Networks
In the case of data augmented networks, we also analyze which layers help the network to encode the rotational invariance, which is important for understanding its limitations and how to best retrain a network with data augmentation to achieve invariance to rotation.
