Large, pretrained models are commonly finetuned with imagery that is heavily augmented to
mimic different conditions and scales, with the resulting models used for various tasks with …

In computer vision, convolutional networks (CNNs) often adopt pooling to enlarge receptive
field which has the advantage of low computational complexity. However, pooling can cause …

It is often a difficult task to accurately segment images with intensity inhomogeneity, because
most of representative algorithms are region-based that depend on intensity homogeneity of …

With the rapid development of the satellite sensor technology, high spatial resolution remote
sensing (HSR) data have attracted extensive attention in military and civilian applications. In …

Scene recognition plays an important role in the task of visual information retrieval,
segmentation and image/video understanding. Traditional approaches for scene recognition …

Remote sensing images present unique challenges to image analysis due to the extensive
geographic coverage, hardware limitations, and misaligned multi-scale images. This paper …

Metric learning can be very useful to improve the performance of a distance-dependent
classifier. However, separating metric learning from the classifier learning possibly …

The explosive growth of multimedia data on the Internet makes it essential to develop
innovative machine learning algorithms for practical applications especially where only a …

Metric learning aims to learn an appropriate distance metric for a given machine learning
task. Despite its impressive performance in the field of image recognition, it may still not be …

Scene classification has become an effective way to interpret the High Spatial Resolution
(HSR) remote sensing images. Recently, Convolutional Neural Networks (CNN) have been …