UAA-GAN generates query-specific adversarial perturbations via unsupervised GAN training that reduce retrieval accuracy in deep feature spaces while keeping changes visually small.
Deep residual learning for image recognition,
3 Pith papers cite this work. Polarity classification is still indexing.
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PH-GCN constructs a hierarchical graph of person parts and performs local/global feature learning via message passing in an end-to-end network for person re-identification.
A survey of deep learning architectures for 3D sensed data classification covering RGB-D, multi-view, volumetric and end-to-end methods along with datasets and future directions.
citing papers explorer
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Unsupervised Adversarial Attacks on Deep Feature-based Retrieval with GAN
UAA-GAN generates query-specific adversarial perturbations via unsupervised GAN training that reduce retrieval accuracy in deep feature spaces while keeping changes visually small.
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PH-GCN: Person Re-identification with Part-based Hierarchical Graph Convolutional Network
PH-GCN constructs a hierarchical graph of person parts and performs local/global feature learning via message passing in an end-to-end network for person re-identification.
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A review on deep learning techniques for 3D sensed data classification
A survey of deep learning architectures for 3D sensed data classification covering RGB-D, multi-view, volumetric and end-to-end methods along with datasets and future directions.