8 hours ago · Before CNNs, the standard way to train a neural network to classify images was to flatten it into a list of pixels and pass it through a feed-forward neural ...
24 hours ago · Abstract and Figures. In the realm of resource-constrained mobile vision tasks, the pursuit of efficiency and performance consistently drives innovation in ...
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19 hours ago · [8] proposed a deep learning based gearbox fault diagnosis method that addresses data scarcity. [9] have fused multiple vibration signal into two- dimensional ...
9 hours ago · With the advent of convolutional neural networks (CNNs) [29, 3] ... Internvid: A large-scale video-text dataset for multimodal understanding and generation.
20 hours ago · Variable batch size strategy in large-scale distributed CNN training shows that different stages benefit from different batch sizes, maintaining high accuracy ...
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24 hours ago · Abstract. Semi-supervised medical image segmentation presents a compelling approach to streamline large-scale image analysis, alleviating annotation burdens ...
2 hours ago · Typically, classification techniques for this type of learning will either work with classifying patches, small pieces of images, to classify the entire image, ...
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4 hours ago · 2, SLS can be built hierarchically, beginning with the assembly of individual neurons into functional units called chunks. These chunks are then integrated into ...
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24 hours ago · Multimodal multilabel classification (MMC) is a challenging task that aims to design a learning algorithm to handle two data sources, the image and text, ...
19 hours ago · Faster R-CNN approaches object detection as a two-stage problem: generating region proposals and then classifying those regions. However, more recent algorithms ...