The development of learning-based hyperspectral image compression methods has
recently attracted great attention in remote sensing. Such methods require a
high number of hyperspectral images to be used during training to optimize all
parameters and reach a high compression performance. However, existing
hyperspectral datasets are not sufficient to train and evaluate learning-based
compression methods, which hinders the research in this field. To address this
problem, in this paper we present HySpecNet-11k that is a large-scale
hyperspectral benchmark dataset made up of 11,483 nonoverlapping image patches.
Each patch is a portion of 128 $\times$ 128 pixels with 224 spectral bands and
a ground sample distance of 30 m. We exploit HySpecNet-11k to benchmark the
current state of the art in learning-based hyperspectral image compression by
focussing our attention on various 1D, 2D and 3D convolutional autoencoder
architectures. Nevertheless, HySpecNet-11k can be used for any unsupervised
learning task in the framework of hyperspectral image analysis. The dataset,
our code and the pre-trained weights are publicly available at
this https URL

本研究提出了一种用于学习基于高光谱图像压缩的大规模基准数据集 HySpecNet-11k，包括 11483 个分别为 128×128 像素的图像，具有 224 个光谱波段和 30m 的地面采样距离，用于各种一维，二维和三维卷积自编码器架构的基准测试和未监督学习任务。

HySpecNet-11k: 用于基于学习的高光谱图像压缩方法基准测试的大规模高光谱数据集

HySpecNet-11k: A Large-Scale Hyperspectral Dataset for Benchmarking  Learning-Based Hyperspectral Image Compression Methods

Weakly-supervised Temporal Action Localization (WS-TAL) methods learn to
localize temporal starts and ends of action instances in a video under only
video-level supervision. Existing WS-TAL methods rely on deep features learned
for action recognition. However, due to the mismatch between classification and
localization, these features cannot distinguish the frequently co-occurring
contextual background, i.e., the context, and the actual action instances. We
term this challenge action-context confusion, and it will adversely affect the
action localization accuracy. To address this challenge, we introduce a
framework that learns two feature subspaces respectively for actions and their
context. By explicitly accounting for action visual elements, the action
instances can be localized more precisely without the distraction from the
context. To facilitate the learning of these two feature subspaces with only
video-level categorical labels, we leverage the predictions from both spatial
and temporal streams for snippets grouping. In addition, an unsupervised
learning task is introduced to make the proposed module focus on mining
temporal information. The proposed approach outperforms state-of-the-art WS-TAL
methods on three benchmarks, i.e., THUMOS14, ActivityNet v1.2 and v1.3
datasets.

通过学习两个特征子空间（分别专门用于行为和背景）并利用时空运动预测来促进两个特征子空间的学习，本文提出了一种解决弱监督时间动作本地化的方法，该方法在 THUMOS14、ActivityNet v1.2 和 v1.3 数据集上优于现有的 WS-TAL 方法。