基于深度神经网络和隐马尔可夫模型的多年度作物类型分类贝叶斯建模

变换器与卷积模型在卫星图像时间序列作物分割中的比较研究

A Comparative Study of Transformer and Convolutional Models for Crop Segmentation from Satellite Image Time Series

本文比较了变换器和卷积模型在从卫星图像时间序列中进行作物分割中的应用，发现TSViT在整体表现上最佳，而VistaFormer在效率与性能之间提供了良好的权衡。

HiDDeN: Hiding Data With Deep Networks

Recent work has shown that deep neural networks are highly sensitive to tiny perturbations of input images, giving rise to adversarial examples. Though this property is usually considered a weakness of learned models, we explore whether it can be beneficial. We find that neural networks can learn to use invisible perturbations to encode a rich amount of useful information. In fact, one can exploit this capability for the task of data hiding. We jointly train encoder and decoder networks, where given an input message and cover image, the encoder produces a visually indistinguishable encoded ima…

Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the pro…

Normal Similarity Network for Generative Modelling

Gaussian distributions are commonly used as a key building block in many generative models. However, their applicability has not been well explored in deep networks. In this paper, we propose a novel deep generative model named as Normal Similarity Network (NSN) where the layers are constructed with Gaussian-style filters. NSN is trained with a layer-wise non-parametric density estimation algorithm that iteratively down-samples the training images and captures the density of the down-sampled training images in the final layer. Additionally, we propose NSN-Gen for generating new samples from no…

使用循环网络实现视频超分辨率的视频无关稳定化的时间序列初始化与条件化

Time-series Initialization and Conditioning for Video-agnostic Stabilization of Video Super-Resolution using Recurrent Networks

本文提出一种用于视频超分辨率的循环网络训练策略，通过重用依视频属性变化的隐藏状态并引入帧数条件化，有效消除了长短视频间的域差距，实现了独立于视频长度和动态特性的高效稳定超分。

Multi-scale Visual Attention & Saliency Modelling with Decision Theory

Bottom-up saliency, an early human visual processing, behaves like binary classification of interest and null hypothesis. Its discriminant power, mutual information of image features and class distribution, is closely related to saliency value by the well-known centre-surround theory. As classification accuracy very much depends on window sizes, the discriminant saliency (power) varies according to sampling scales. Discriminating power estimation in multi-scales framework needs integrating with wavelet transformation and then estimating statistical discrepancy of two consecutive scales (centre…

Trustworthy modelling of atmospheric formaldehyde powered by deep learning

Formaldehyde (HCHO) is one one of the most important trace gas in the atmosphere, as it is a pollutant causing respiratory and other diseases. It is also a precursor of tropospheric ozone which damages crops and deteriorates human health. Study of HCHO chemistry and long-term monitoring using satellite data is important from the perspective of human health, food security and air pollution. Dynamic atmospheric chemistry models struggle to simulate atmospheric formaldehyde and often overestimate by up to two times relative to satellite observations and reanalysis. Spatial distribution of modelle…

使用隐马尔可夫模型的点云数据运动物体分割

Moving Object Segmentation in Point Cloud Data using Hidden Markov Models

针对点云动态检测中的不确定性与场景泛化问题，提出一种基于逐体素HMM建模和HMM滤波地图融合的免学习运动物体分割方法，在基准数据集上达到或超过现有方法。