Search Results for author:
Found 10 papers, 3 papers with code
Patch Spatio-Temporal Relation Prediction for Video Anomaly Detection
To mitigate memory consumption, we convert the order information prediction task into a multi-label learning problem, and the inter-patch similarity prediction task into a distance matrix regression problem.
Unsupervised Collaborative Metric Learning with Mixed-Scale Groups for General Object Retrieval
This paper presents a novel unsupervised deep metric learning approach, termed unsupervised collaborative metric learning with mixed-scale groups (MS-UGCML), devised to learn embeddings for objects of varying scales.
POAR: Towards Open Vocabulary Pedestrian Attribute Recognition
Our key idea is to formulate the POAR problem as an image-text search problem.
Contrastive Bayesian Analysis for Deep Metric Learning
Recent methods for deep metric learning have been focusing on designing different contrastive loss functions between positive and negative pairs of samples so that the learned feature embedding is able to pull positive samples of the same class closer and push negative samples from different classes away from each other.
Coded Residual Transform for Generalizable Deep Metric Learning
To address this challenge, in this paper, we introduce a new method called coded residual transform (CRT) for deep metric learning to significantly improve its generalization capability.
LighTN: Light-weight Transformer Network for Performance-overhead Tradeoff in Point Cloud Downsampling
Compared with traditional task-irrelevant downsampling methods, task-oriented neural networks have shown improved performance in point cloud downsampling range.
Relative Order Analysis and Optimization for Unsupervised Deep Metric Learning
During training, this relative order prediction network and the feature embedding network are tightly coupled, providing mutual constraints to each other to improve overall metric learning performance in a cooperative manner.
Attention Models for Point Clouds in Deep Learning: A Survey
Recently, the advancement of 3D point clouds in deep learning has attracted intensive research in different application domains such as computer vision and robotic tasks.
A GAN-Based Input-Size Flexibility Model for Single Image Dehazing
Based on the atmospheric scattering model, a novel model is designed to directly generate the haze-free image.
Progressive Neural Networks for Image Classification
The inference structures and computational complexity of existing deep neural networks, once trained, are fixed and remain the same for all test images.
