The success of deeplearning 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, …
S Long, X He, C Yao - International Journal of Computer Vision, 2021 - Springer
… In summary, text detection and recognition methods before the deeplearningera 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 deeplearning, 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 deeplearningera, … in the hope that a joint task learning could result in better generalization …
… fast development of prevalent deeplearning methods, there is … close sibling in the deep learningera, instance segmentation. … and label limitations in this deeplearningera. Further, we …
… Since the first introduction in 2015 [27], [28], [29], deeplearning-based SOD (or deep SOD) … in the deeplearningera. 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 deeplearning … using deeplearning …
… of deeplearning and non-deeplearning attention models, a … study which enables a deeper understanding of the results and … Further, in Section 5.4, we provide deeper insights into the …