Search Results for author:
Found 8 papers, 3 papers with code
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.
Spatial Assembly Networks for Image Representation Learning
It has been long recognized that deep neural networks are sensitive to changes in spatial configurations or scene structures.
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.
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.
Unsupervised Deep Metric Learning with Transformed Attention Consistency and Contrastive Clustering Loss
To further enhance the inter-class discriminative power of the feature generated by this network, we adapt the concept of triplet loss from supervised metric learning to our unsupervised case and introduce the contrastive clustering loss.
