… Image Geolocalization using Deep Learning Given a large training data of images with
GPS labels, we examine two deep learning approaches for geolocalization. For both cases we …

The success of deep learning in vision can be attributed to:(a) models with high capacity;(b)
increased computational power; and (c) availability of large-scale labeled data. Since 2012, …

… In summary, text detection and recognition methods before the deep learning era mainly
extract low-level or mid-level handcrafted image features, which entails demanding and …

The ability to cooperate through language is a defining feature of humans. As the perceptual,
motory and planning capabilities of deep artificial networks increase, researchers are …

… Over the past years, the development of bioimage analysis tools, including deep learning,
is changing how we perform live imaging. Here we briefly cover important computational …

… learning (MTL) models that given an input image can infer all desired task outputs. Before the
deep learning era, … in the hope that a joint task learning could result in better generalization …

… fast development of prevalent deep learning methods, there is … close sibling in the deep
learning era, instance segmentation. … and label limitations in this deep learning era. Further, we …

… Since the first introduction in 2015 [27], [28], [29], deep learning-based SOD (or deep SOD)
… in the deep learning era. In addition to taxonomically reviewing existing deep SOD methods, …

… era of rapid evolution, this article provides a comprehensive survey of the recent developments
in this field. We focus on the works which use deep learning … using deep learning …

… of deep learning and non-deep learning attention models, a … study which enables a deeper
understanding of the results and … Further, in Section 5.4, we provide deeper insights into the …