Detection noise significantly degrades the quality of structured illumination microscopy (SIM) images, especially under low-light conditions. Although supervised learning based denoising methods have shown prominent advances in eliminating the noise-induced artifacts, the requirement of a large amount of hig...
Self-supervised learning旨在通过由图像/patch操作和spatial-temporal操作构建pre-text task从未标记的视觉数据集中学习。对比学习利用图像和视频的增强不变性。对于视觉,已经提出不同的掩码预测目标。MAE利用有效的非对称结构预测像素颜色,BeiT和iBOT预测的pre-dict dVAE或可学习的token。MaskFeat预测HOG特征,data2vec从mo...
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Detection noise significantly degrades the quality of structured illumination microscopy (SIM) images, especially under low-light conditions. Although supervised learning based denoising methods have shown prominent advances in eliminating the noise-indu
Here, we describe a data-efficient, deep learning-based denoising solution to improve diverse SR imaging modalities. The method, SN2N, is a Self-inspired Noise2Noise module with self-supervised data generation and self-constrained learning process. SN2N is fully competitive with supervised learning...
In our work, we propose a novel self-supervised method, which effectively trains a 3D human pose estimation network without any extra 3D pose annotations. Different from the commonly used GAN-based technique, our method overcomes the projection ambiguity problem by fully disentangling the camera ...
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* 题目: Self-Supervised Video Similarity Learning* PDF: arxiv.org/abs/2304.0337* 作者: Giorgos Kordopatis-Zilos,Giorgos Tolias,Christos Tzelepis,Ioannis Kompatsiaris,Ioannis Patras,Symeon Papadopoulos* 相关: github.com/gkordo/s2vs 视频处理-其他 1篇 * 题目: Therbligs in Action: Video Understandin...
Although there have been a few attempts in training an image denoising model with only single noisy images, existing self-supervised denoising approaches suffer from inefficient network training, loss of useful information, or dependence on noise modeling. In this paper, we present a very simple ...