Search Results for author:
Found 11 papers, 5 papers with code
FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition
Current state-of-the-art deep learning based face recognition (FR) models require a large number of face identities for central training.
Adaptive Region Pooling for Fine-Grained Representation Learning
Fine-grained recognition aims to discriminate the sub-categories of the images within one general category.
Interactive Object Segmentation with Dynamic Click Transform
In the interactive segmentation, users initially click on the target object to segment the main body and then provide corrections on mislabeled regions to iteratively refine the segmentation masks.
Hard Samples Rectification for Unsupervised Cross-domain Person Re-identification
Person re-identification (re-ID) has received great success with the supervised learning methods.
Video-based Person Re-identification without Bells and Whistles
Besides, we discover the errors not only for the identity labels of tracklets but also for the evaluation protocol for the test data of MARS.
Viewpoint-Aware Channel-Wise Attentive Network for Vehicle Re-Identification
Our VCAM enables the feature learning framework channel-wisely reweighing the importance of each feature maps according to the "viewpoint" of input vehicle.
Semantics-Guided Clustering with Deep Progressive Learning for Semi-Supervised Person Re-identification
As a result, our approach is able to augment the labeled training data in the semi-supervised setting.
Orientation-aware Vehicle Re-identification with Semantics-guided Part Attention Network
Vehicle re-identification (re-ID) focuses on matching images of the same vehicle across different cameras.
Spatially and Temporally Efficient Non-local Attention Network for Video-based Person Re-Identification
Video-based person re-identification (Re-ID) aims at matching video sequences of pedestrians across non-overlapping cameras.
Ranked #11 on
Person Re-Identification
on MARS
Increasing Compactness Of Deep Learning Based Speech Enhancement Models With Parameter Pruning And Quantization Techniques
In addition, a parameter quantization (PQ) technique was applied to reduce the size of a neural network by representing weights with fewer cluster centroids.
Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal
Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it.
