Summary of An Evolutionary Network Architecture Search Framework with Adaptive Multimodal Fusion For Hand Gesture Recognition, by Yizhang Xia et al.
An Evolutionary Network Architecture Search Framework with Adaptive Multimodal Fusion for Hand Gesture Recognition
by Yizhang Xia, Shihao Song, Zhanglu Hou, Junwen Xu, Juan Zou, Yuan Liu, Shengxiang Yang
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 hand gesture recognition (HGR) based on multimodal data using an evolutionary network architecture search framework with adaptive multimodel fusion (AMF-ENAS). The proposed method simultaneously considers fusion positions and ratios of the multimodal data, allowing for automatic construction of multimodal networks with different architectures. The approach is designed to adapt to various datasets and tackle issues related to the fusion position and ratio of multimodal data. Experimental results demonstrate that AMF-ENAS achieves state-of-the-art performance on three benchmark datasets: Ninapro DB2, DB3, and DB7. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to recognize hand gestures using many types of data at once. It’s like trying to figure out what someone is saying by looking at their face, hands, and voice all together! The researchers made a special computer program that can learn to combine different types of information in the best way possible. They tested it on some big databases and it did really well! |
Keywords
» Artificial intelligence » Gesture recognition
