Search Results for author:
Found 5 papers, 2 papers with code
Designing Deep Networks for Scene Recognition
Most deep learning backbones are evaluated on ImageNet.
Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo
Contrastive learning (CL) is widely known to require many negative samples, 65536 in MoCo for instance, for which the performance of a dictionary-free framework is often inferior because the negative sample size (NSS) is limited by its mini-batch size (MBS).
Urban land-use analysis using proximate sensing imagery: a survey
Urban regions are complicated functional systems that are closely associated with and reshaped by human activities.
DCANet: Learning Connected Attentions for Convolutional Neural Networks
With DCANet, all attention blocks in a CNN model are trained jointly, which improves the ability of attention learning.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
