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Jie Wang, Jie Yang, Jiafan He and Dongliang Peng
Semi-supervised learning has been proven to be effective in utilizing unlabeled samples to mitigate the problem of limited labeled data. Traditional semi-supervised learning methods generate pseudo-labels for unlabeled samples and train the classifier us...
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Swarnendu Ghosh, Teresa Gonçalves and Nibaran Das
Conceptual representations of images involving descriptions of entities and their relations are often represented using scene graphs. Such scene graphs can express relational concepts by using sets of triplets ⟨subject—predicate&...
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Sangwon Lee, Hyemi Kim and Gil-Jin Jang
Audio classification; music information retrieval; audio scene characterization; temporal localization of sound sources; audio indexing; audio surveillance systems; anomaly detection from audio sounds.
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Aye Aye Mar, Kiyoaki Shirai and Natthawut Kertkeidkachorn
Aspect-based sentiment analysis (ABSA) is a process to extract an aspect of a product from a customer review and identify its polarity. Most previous studies of ABSA focused on explicit aspects, but implicit aspects have not yet been the subject of much ...
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Semen Mukhamadiev, Sergey Nesteruk, Svetlana Illarionova and Andrey Somov
Plant segmentation is a challenging computer vision task due to plant images complexity. For many practical problems, we have to solve even more difficult tasks. We need to distinguish plant parts rather than the whole plant. The major complication of mu...
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David Kartchner, Davi Nakajima An, Wendi Ren, Chao Zhang and Cassie S. Mitchell
A major bottleneck preventing the extension of deep learning systems to new domains is the prohibitive cost of acquiring sufficient training labels. Alternatives such as weak supervision, active learning, and fine-tuning of pretrained models reduce this ...
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Yaojie Zhang, Huahu Xu, Junsheng Xiao and Minjie Bian
The real world is full of noisy labels that lead neural networks to perform poorly because deep neural networks (DNNs) are prone to overfitting label noise. Noise label training is a challenging problem relating to weakly supervised learning. The most ad...
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Dongwei Qiu, Haorong Liang, Zhilin Wang, Yuci Tong and Shanshan Wan
Quickly and accurately identifying water leakage is one of the important components of the health monitoring of subway tunnels. A mobile vision measurement system consisting of several high-resolution, industrial, charge-coupled device (CCD) cameras is p...
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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 ...
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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...
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