3 accepted papers
Most structures of deep neural networks (DNN) are with a fixed complexity of both computational cost (parameters and FLOPs) and the expressiveness. In this work, we experimentally investigate the effectiveness of using neural ordinary differential equations (NODEs) as a component to provide further…
Semantic segmentation is a fundamental task in computer vision and image processing. Although existing methods based on the fully convolutional network (FCN) have greatly improved the accuracy, it still does not show satisfactory results on tiny objects and boundary regions. One of the problems is t…
Hyperspectral images (HSIs) consists of 2D spatial information and 1D spectral signature due to its specialty. Most models take the raw spectral signature as the input directly by regarding the spectral data as a sequence, which cannot fully explore the redundant and complementary information inside…