Knowledge-Preserving continual person re-identification using Graph Attention Network
Person re-identification (ReID), considered as a sub-problem of image retrieval, is critical for intelligent security. The general practice is to train a deep model on images from a particular scenario (also known as a domain) and ...
VISAL—A novel learning strategy to address class imbalance
In the imbalance data scenarios, Deep Neural Networks (DNNs) fail to generalize well on minority classes. In this letter, we propose a simple and effective learning function i.e, Visually Interpretable Space Adjustment Learning (VISAL) ...
Learning matrix factorization with scalable distance metric and regularizer
Matrix factorization has always been an encouraging field, which attempts to extract discriminative features from high-dimensional data. However, it suffers from negative generalization ability and high computational complexity when ...
Highlights
- Propose a learnable method to approximate several popular matrix factorizations.
BalanceHRNet: An effective network for bottom-up human pose estimation
In the study of human pose estimation, which is widely used in safety and sports scenes, the performance of deep learning methods is greatly reduced in high overlap rate and crowded scenes. Therefore, we propose a bottom-up model, ...
MixGradient: A gradient-based re-weighting scheme with mixup for imbalanced data streams
A challenge for contemporary deep neural networks in real-world problems is learning from an imbalanced data stream, where data tends to be received chunk by chunk over time, and the prior class distribution is severely imbalanced. ...
Eigen value based loss function for training attractors in iterated autoencoders
The way that the human brain handles the input variations has been one of the most interesting areas of research for neuroscientists. There are some evidences that the human brain acts like an attractor when trying to memorize or ...
