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Found 4 papers, 2 papers with code
SWIPENET: Object detection in noisy underwater images
Moreover, inspired by the human education process that drives the learning from easy to hard concepts, we here propose the CMA training paradigm that first trains a clean detector which is free from the influence of noisy data.
Underwater object detection using Invert Multi-Class Adaboost with deep learning
In addition, we propose a novel sample-weighted loss function which can model sample weights for SWIPENet, which uses a novel sample re-weighting algorithm, namely Invert Multi-Class Adaboost (IMA), to reduce the influence of noise on the proposed SWIPENet.
Learning spatiotemporal representations for human fall detection in surveillance video
Finally, we apply voting on the results of event classification to perform multicamera fall detection.
Cascade one-vs-rest detection network for fine-grained recognition without part annotations
To bridge this gap, we introduce a cascaded structure to eliminate background and exploit a one-vs-rest loss to capture more minute variances among different subordinate categories.
