|
|
|
|
|
Mustafa Kara, Zeynep Öztürk, Sergin Akpek and Aysegül Turupcu
Advancements in deep learning and availability of medical imaging data have led to the use of CNN-based architectures in disease diagnostic assisted systems. In spite of the abundant use of reverse transcription-polymerase chain reaction-based tests in C...
ver más
|
|
|
|
|
|
|
|
|
Shurong Sheng, Katrien Laenen, Luc Van Gool and Marie-Francine Moens
In this paper, we target the tasks of fine-grained image?text alignment and cross-modal retrieval in the cultural heritage domain as follows: (1) given an image fragment of an artwork, we retrieve the noun phrases that describe it; (2) given a noun phras...
ver más
|
|
|
|
|
|
|
|
|
Shikha Dubey, Abhijeet Boragule, Jeonghwan Gwak and Moongu Jeon
Given the scarcity of annotated datasets, learning the context-dependency of anomalous events as well as mitigating false alarms represent challenges in the task of anomalous activity detection. We propose a framework, Deep-network with Multiple Ranking ...
ver más
|
|
|
|
|
|
|
|
|
Fangxiong Chen, Guoheng Huang, Jiaying Lan, Yanhui Wu, Chi-Man Pun, Wing-Kuen Ling and Lianglun Cheng
The fine-grained image classification task is about differentiating between different object classes. The difficulties of the task are large intra-class variance and small inter-class variance. For this reason, improving models? accuracies on the task he...
ver más
|
|
|
|
|
|
|
|
|
Lukas Tuggener, Mohammadreza Amirian, Fernando Benites, Pius von Däniken, Prakhar Gupta, Frank-Peter Schilling and Thilo Stadelmann
We present an extensive evaluation of a wide variety of promising design patterns for automated deep-learning (AutoDL) methods, organized according to the problem categories of the 2019 AutoDL challenges, which set the task of optimizing both model accur...
ver más
|
|
|
|