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细粒度的语义信息(SwAV是对同一张图的不同视角进行判断) 3)...
@article{baranchuk2021label, title={Label-efficient semantic segmentation with diffusion models}, author={Baranchuk, Dmitry and Rubachev, Ivan and Voynov, Andrey and Khrulkov, Valentin and Babenko, …
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 ...
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 ...
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...
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 ...
The crux of label-efficient semantic segmentation is to produce high-quality pseudo-labels to leverage a large amount of unlabeled or weakly labeled data. A common practice is to select the highly confident predictions as the pseudo-ground-truths for each pixel, but it leads to a problem that...
active learningsemantic segmentationDeep learning models are the state of the art for semantic segmentation of point clouds, the success of which relies on the availability of large-scale annotated datasets. However, it can be prohibitively costly to prepare such datasets. In this work, we propose...
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...