Three-dimensional (3D) integrated renal structures (IRS) segmentation is important in clinical practice.
Validations on the gait recognition metric CASIA-B dataset further demonstrated the capability of our hybrid model.
One major challenge comes from the imbalanced long-tail person identity distributions, which prevents the one-step person search model from learning discriminative person features for the final re-identification.
In the proposed TDN, for better knowledge transfer from the Re-ID teacher model to the one-step person search model, we design a strong one-step person search base framework by partially disentangling the two subtasks.
Electricity load forecasting is crucial for the power systems' planning and maintenance.
This paper analyzes a discretization of a stochastic parabolic optimal control problem, where the diffusion term contains the control variable.
Numerical Analysis Numerical Analysis Optimization and Control
Reversible and controlled uniaxial strain triggers these topological defects, manifested as new quantum Hall effect plateaus as well as a discrete and reversible modulation of the current across the device.
Mesoscale and Nanoscale Physics
To learn the optimal similarity function between probe and gallery images in Person re-identification, effective deep metric learning methods have been extensively explored to obtain discriminative feature embedding.
On each task, we train a few-shot model to predict pseudo labels for unlabeled data, and then iterate the self-training steps on labeled and pseudo-labeled data with each step followed by fine-tuning.
To address this problem, in this paper, we present a robust and efficient graph correspondence transfer (REGCT) approach for explicit spatial alignment in Re-ID.
In this paper, we propose a graph correspondence transfer (GCT) approach for person re-identification.
Meanwhile, a weighting scheme is applied on the bilinear coding to adaptively adjust the weights of local features at different locations based on their importance in recognition, further improving the discriminability of feature aggregation.