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Asymmetric Deep Semantic Quantization for Image Retrieval
However, we argue that the current deep learning based hashing methods ignore some critical problems (e. g., the learned hash codes are not discriminative due to the hashing methods being unable to discover rich semantic information and the training strategy having difficulty optimizing the discrete binary codes).
Asymmetric Residual Neural Network for Accurate Human Activity Recognition
Human Activity Recognition (HAR) using deep neural network has become a hot topic in human-computer interaction.
Deep Attention-guided Hashing
The core idea is to use guided hash codes which are generated by the hashing network of the first stream framework (called first hashing network) to guide the training of the hashing network of the second stream framework (called second hashing network).
DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition
Using the quantization method proposed, we were able to achieve performances closer to that of full-precision counterpart.
