M-SAC-VLADNet: A Multi-Path Deep Feature Coding Model for Visual Classification
AbstractVector of locally aggregated descriptor (VLAD) coding has become an efficient feature coding model for retrieval and classification. In some recent works, the VLAD coding method is extended to a deep feature coding model which is called NetVLAD. NetVLAD improves significantly over the original VLAD method. Although the NetVLAD model has shown its potential for retrieval and classification, the discriminative ability is not fully researched. In this paper, we propose a new end-to-end feature coding network which is more discriminative than the NetVLAD model. First, we propose a sparsely-adaptive and covariance VLAD model. Next, we derive the back propagation models of all the proposed layers and extend the proposed feature coding model to an end-to-end neural network. Finally, we construct a multi-path feature coding network which aggregates multiple newly-designed feature coding networks for visual classification. Some experimental results show that our feature coding network is very effective for visual classification. View Full-Text
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Chen, B.; Li, J.; Wei, G.; Ma, B. M-SAC-VLADNet: A Multi-Path Deep Feature Coding Model for Visual Classification. Entropy 2018, 20, 341.
Chen B, Li J, Wei G, Ma B. M-SAC-VLADNet: A Multi-Path Deep Feature Coding Model for Visual Classification. Entropy. 2018; 20(5):341.Chicago/Turabian Style
Chen, Boheng; Li, Jie; Wei, Gang; Ma, Biyun. 2018. "M-SAC-VLADNet: A Multi-Path Deep Feature Coding Model for Visual Classification." Entropy 20, no. 5: 341.
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