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Found 3 papers, 1 papers with code
Deep Contrastive One-Class Time Series Anomaly Detection
To overcome the shortcomings, a deep Contrastive One-Class Anomaly detection method of time series (COCA) is proposed by authors, following the normality assumptions of CL and one-class classification.
A Dataset And Benchmark Of Underwater Object Detection For Robot Picking
Towards these challenges we introduce a dataset, Detecting Underwater Objects (DUO), and a corresponding benchmark, based on the collection and re-annotation of all relevant datasets.
A New Dataset, Poisson GAN and AquaNet for Underwater Object Grabbing
We also propose a novel Poisson-blending Generative Adversarial Network (Poisson GAN) and an efficient object detection network (AquaNet) to address two common issues within related datasets: the class-imbalance problem and the problem of mass small object, respectively.
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