Panasonic R&D Center Singapore’s Journal Paper Published in the April 2022 Issue of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

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Teaming up with Nanjing University of Science and Technology (NUST), Panasonic R&D Center Singapore co-authored a journal paper titled “Covariance Attention for Semantic Segmentation” which has been published in the April 2022 issue of the IEEE Transactions on Pattern Analysis and Machine Intelligence, the world’s top journal in the field of AI, machine learning, computer vision and pattern recognition. In the article, we present a low-complexity deep learning approach that exploits the covariance matrix to encode the dependencies over local and global cues of the scene. The spatial and channel covariance attention modules are designed, respectively, to boost the accuracy of semantic segmentation (assigning a label to every pixel in the image with a corresponding class). We build a new deep learning network named CANet for semantic segmentation by using the proposed covariance attention modules to achieve the state-of-the-art performance on multiple challenging datasets. This novel AI technology can be used in various practical applications such as autonomous driving, robot navigation, safety monitoring, building inspection, medical image analysis, to name a few.

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