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Faster-YOLO: An accurate and faster object detection method
Multi-view CSPMPR-ELM feature learning and classifying for RGB-D object recognition
This paper introduces a multi-view CNN-SPMP-RNN-ELM (MCSPMPR- ELM) model for RGB-D object recognition, which combines the power of MCSPM PR and fast training of ELM, and achieves competitive performance compared with other state-of-the-art algorithms specifically designed forRGB-D data.
Multi-model convolutional extreme learning machine with kernel for RGB-D object recognition
An effective multi-modal convolutional extreme learning machine with kernel (MMC-KELM) structure, which combines advantages both the power of CNN and fast training of ELM to achieve high generalization performance with faster learning speed is proposed.
RGB-D object recognition based on the joint deep random kernel convolution and ELM
A Joint Deep Radom Kernel Convolution and ELM (JDRKC-ELM) method for object recognition, which integrating the power of CNN feature extraction and fast training of ELM-AE achieves high recognition accuracy and good generalization performance in comparison with deep learning methods and other ELM methods.