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Found 3 papers, 1 papers with code
Deep Feature Augmentation for Occluded Image Classification
To alleviate the dependency on large-scale occluded image datasets, we propose a novel approach to improve the classification accuracy of occluded images by fine-tuning the pre-trained models with a set of augmented deep feature vectors (DFVs).
Boosting Occluded Image Classification via Subspace Decomposition Based Estimation of Deep Features
By employing the ResNet-152, pre-trained on the ILSVRC2012 training set, as the base network, the proposed SBDE-based classification scheme is extensively evaluated on the Caltech-101 and ILSVRC2012 datasets.
MDFN: Multi-Scale Deep Feature Learning Network for Object Detection
Compared with features produced in earlier layers, the deep features are better at expressing semantic and contextual information.
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