# ASL Recognition with Metric-Learning based Lightweight Network

Source: [ASL Recognition with Metric-Learning based Lightweight Network](<https://www.sota2.com/research/paper/68fa67541e209131cea15895>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:11:14.110Z

Authors: Evgeny Izutov

Published: 2020-04-10T14:41:30.000Z

## Abstract

In the past decades the set of human tasks that are solved by machines was extended dramatically. From simple image classification problems researchers now move towards solving more sophisticated and vital problems, like, autonomous driving and language translation. The case of language translation includes a challenging area of sign language translation that incorporates both image and language processing. We make a step in that direction by proposing a lightweight network for ASL gesture recognition with a performance sufficient for practical applications. The proposed solution demonstrates impressive robustness on MS-ASL dataset and in live mode for continuous sign gesture recognition scenario. Additionally, we describe how to combine action recognition model training with metric-learning to train the network on the database of limited size. The training code is available as part of Intel OpenVINO Training Extensions.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.05054>)

[PDF](<https://arxiv.org/pdf/2004.05054.pdf>)