@article{baranchuk2021label, title={Label-efficient semantic segmentation with diffusion models}, author={Baranchuk, Dmitry and Rubachev, Ivan and Voynov, Andrey and Khrulkov, Valentin and Babenko, …
4)使用合成数据集训练其他语义分割方法,并在真实图像上进行测试 在本文中作者称之为“Few shot semantic segmentation methods”,并用DDPM的生成器替换了StyleGAN生成器进行了第二组实验。 所以这个大表能看出如下结论: 1)DDPM有着最好的语义分割结果;MAE紧随其后; 2)SwAV性能不佳:用判别式的方法进行预训练会压缩...
While most prior works focus on indoor scenes, we are one of the first to propose a label-efficient semantic segmentation pipeline for outdoor scenes with LiDAR point clouds. Our method co-designs an efficient labeling process with semi/weakly supervised learning and is applicable to nearly any ...
In this paper, we demonstrate that diffusion models can also serve as an instrument for semantic segmentation, especially in the setup when labeled data is scarce. In particular, for several pretrained diffusion models, we investigate the intermediate activations from the networks that perform the ...
Official implementation of the paper Label-Efficient Semantic Segmentation with Diffusion ModelsThis code is based on datasetGAN and guided-diffusion.Note: use --recurse-submodules when clone.OverviewThe paper investigates the representations learned by the state-of-the-art DDPMs and shows that they ...
Due to the randomness of functiontorch.nn.functional.interpolatewhenmode="bilinear", the results of semantic segmentation will not be the same EVEN IF a fixed random seed is set. Therefore, we recommend you run 3 times and get the average performance. ...
Domain adaptation across scene-level point clouds is thus even more challenging and it has recently attracted increasing attention thanks to the great values of scene-level 3D tasks such as 3D object detection and 3D semantic segmentation. Domain adaptive 3D object detection has been studied ...
系统标签: domainstaskslearninglabeldomainimagenet LabelEfficientLearningofTransferable RepresentationsacrossDomainsandTasks ZelunLuo StanfordUniversity zelunluo@stanford.edu YuliangZou VirginiaTech ylzou@vt.edu JudyHoffman UniversityofCalifornia,Berkeley jhoffman@eecs.berkeley.edu LiFei-Fei StanfordUniversity fe...
Collecting labeled data for the task of semantic segmentation is expensive and time-consuming, as it requires dense pixel-level annotations. While recent Convolutional Neural Network (CNN) based semantic segmentation approaches have achieved impressive results by using large amounts of labeled training da...
在CVCEndoSceneStill和AS-OCT数据集上进行的大量实验和分析表明,当只给出少量强注释实例时,我们的框架能够从大量弱注释实例中学习,并获得与相应的完全监督版本接近的性能。 相关性工作 Weakly Supervised Semantic Segmentation. 弱监督语义分割旨在通过利用低成本标签来减少注释工作(Lin et al 2016;Dai, He, Sun 2015...