Title: Masked Siamese Networks for Label-Efficient LearningFrom Facebook.ArXiv 2022.04.14 Highlight 本文提出了一种新的自监督pretrain的方法,Masked Siamese Networks(MSE)。实验发现这个pretrain在100% 的数据都有label的情况下的效果和MoCo-v3等一众自监督方法效果差不多,同时作者发现在极度semi-supervised的set...
In this paper, we investigate the use of Approximate Convex Decompositions (ACD) as a self-supervisory signal for label-efficient learning of point cloud representations. We show that using ACD to approximate ground truth segmentation provides excellent self-supervision for learning 3D point cloud ...
综述标题:Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions 论文链接:[2303.12484] Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions (arxiv.org) 摘要: 近年来,深度学习发展迅速,并在广泛的应用中取得了最先进的性能。然而,训练模...
Security Insights Additional navigation options main 1Branch0Tags Code MSNMaskedSiameseNetworks This repo provides a PyTorch implementation of MSN (MaskedSiameseNetworks), as described in the paperMasked Siamese Networks for Label-Efficient Learning. ...
We present LAVA, a simple yet effective method for multi-domain visual transfer learning with limited data. LAVA builds on a few recent innovations to enable adapting to partially labelled datasets with class and domain shifts. First, LAVA learns self-supervised visual representations on the source...
Correction: Investigating Self-Supervised Methods for Label-Efficient Learning doi:10.1007/s11263-025-02455-xSpringer USInternational Journal of Computer VisionNandam, Srinivasa RaoSurrey Institute for People-Centred AI (PAI), University of Surrey, Guildford, UKAtito, SaraCentre for Vision, Speech and...
This paper presents the first comprehensive survey of label-efficient learning of point clouds. We address three critical questions in this emerging research field: i) the importance and urgency of label-efficient learning in point cloud processing, ii) the subfields it encompasses, and iii) the ...
LAVA: Label-efficient Visual Learning and Adaptation Islam Nassar1*, Munawar Hayat1, Ehsan Abbasnejad2, Hamid Rezatofighi1, Mehrtash Harandi1, Gholamreza Haffari1 1 Monash University, Australia 2 University of Adelaide, Australia Abstract We present LAVA, a simple yet effective method...
Paper tables with annotated results for Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation
Welcome to LabelBench, where we evaluate label-efficient learning performance with a concerted combination of large pretrained models, semi-supervised learning and active learning algorithms. We encourage researchers to contribute datasets, pretrained models, semi-supervised training algorithms and active lear...