1. 论文介绍 Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification Sangmin Bae1,5∗, June-Woo Kim2,5∗, Won-Yang Cho3,5, Hyerim Baek3,5, Soyoun Son5, Byungjo Lee4,5, Changwan Ha5, Kyongpil Tae5, Sungnyun Kim1,5†, Se-Young Yun1...
论文题目:Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning / PACL 论文地址:http://arxiv.org/abs/2212.04994 --- 第二篇:CLIPpy: Perceptual Grouping in Contrastive Vision-Language Models 论文:http://arxiv.org/abs/2210.09996 代码:https://github.com/kahnchana/clippy * 本...
论文:https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Cross-Patch_Dense_Contrastive_Learning_for_Semi-Supervised_Segmentation_of_Cellular_Nuclei_CVPR_2022_paper.pdf 代码:https://github.com/zzw-szu/CDCL 一、 方法概述 简单地将,这篇文章就是在一个Teacher-Student的架构下解决了半监督分割问题。
Contrastive learningPatch-level methodAn effective approach for the automatic identification of respiratory sounds is presented in this paper, which is helpful to assist in the preliminary diagnosis for respiratory diseases.Differently from most methods that focus on the distribution of entire samples, ...
In this article, we propose multipatch contrastive learning framework for multivariate time-series anomaly detection (MPFormer), a transformer-based multipatch contrastive learning framework. This novel framework leverages contrastive learning to guide discriminative feature learning. It incorporates a data ...
PyTorch implementation and pre-trained models for paper APS: Asymmetric Patch Sampling for Contrastive Learning.APS is a novel asymmetric patch sampling strategy for contrastive learning, to further boost the appearance asymmetry for better representations. APS significantly outperforms the existing self-sup...
论文阅读:cvpr2021 :Joint Generative and Contrastive Learning for Unsupervised Person Re-identification 将GAN、contrastive module 联合学习 GAN 利用身份特征和mesh-based映射视角生成新的图像(图2b) contrastive module从feature memory 中提取正负对,便于视角不变的特征学习(图2c) code: https://github.com/...
In addition, most of the current deep learning-based methods are not lightweight enough, which prompts us to design a more efficient framework for anomaly detection. In this study, we introduce PatchAD, a novel multi-scale patch-based MLP-Mixer architecture that leverages contrastive learning for...
(ASP), to directly deal with the input to target inconsistencies in a proposed H&E-to-IHC image-to-image translation framework. The ASP loss is built upon a patch-based contrastive learning criterion, named Supervised PatchNCE (SP), and augments it further with weight scheduling to mitigate ...
摘要小样本学习的目的是利用从源类中学习到的先验知识来识别目标类,这种知识通常存在于一个深度嵌入模型中,用于支持和查询图像对的一般匹配目的。本文的目的是将这种匹配的对比学习方法转化为学习一个小样本嵌入…