Summary of Isign: a Benchmark For Indian Sign Language Processing, by Abhinav Joshi and Romit Mohanty and Mounika Kanakanti and Andesha Mangla and Sudeep Choudhary and Monali Barbate and Ashutosh Modi
iSign: A Benchmark for Indian Sign Language Processing
by Abhinav Joshi, Romit Mohanty, Mounika Kanakanti, Andesha Mangla, Sudeep Choudhary, Monali Barbate, Ashutosh Modi
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper addresses the limited resources for developing machine learning and data-driven approaches for automated Indian Sign Language (ISL) processing. Despite significant progress in text/audio-based language processing, ISL remains underserved due to the need for more resources. To bridge this gap, the authors introduce iSign, a benchmark for ISL processing, which includes three primary contributions: releasing one of the largest ISL-English datasets with over 118K video-sentence/phrase pairs, proposing multiple NLP-specific tasks (SignVideo2Text, SignPose2Text, Text2Pose, Word Prediction, and Sign Semantics), and providing detailed insights into the proposed benchmarks. These efforts aim to streamline the evaluation of Sign Language processing, addressing gaps in the NLP research community for ISL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new benchmark for Indian Sign Language (ISL) processing to help bridge the gap between text/audio-based language processing and sign languages. It releases a large dataset with over 118K video-sentence/phrase pairs, which is one of the largest datasets available for ISL. The authors also propose several NLP-specific tasks and provide insights into how ISL works. |
Keywords
» Artificial intelligence » Machine learning » Nlp » Semantics
