Summary of Event Stream Based Sign Language Translation: a High-definition Benchmark Dataset and a New Algorithm, by Xiao Wang et al.
Event Stream based Sign Language Translation: A High-Definition Benchmark Dataset and A New Algorithm
by Xiao Wang, Yao Rong, Fuling Wang, Jianing Li, Lin Zhu, Bo Jiang, Yaowei Wang
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to Sign Language Translation (SLT) using high-definition Event streams, which mitigates issues such as lighting and motion blur. The proposed method leverages the Mamba model’s ability to integrate temporal information of CNN features, achieving improved SLT outcomes. A new dataset, Event-CSL, is introduced, containing 14,827 videos, 14,821 glosses, and 2,544 Chinese words in a variety of indoor and outdoor scenes. The paper benchmarks existing mainstream SLT works for fair comparison and releases the benchmark dataset and source code. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps people with disabilities by making it easier to translate sign language into spoken language using special cameras called Event streams. These cameras can handle low light and moving hands, which is important because traditional methods don’t work well in these situations. The researchers also created a new dataset of sign language videos and released the code for other scientists to use. |
Keywords
» Artificial intelligence » Cnn » Translation
