5,985 research outputs found
Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation
Weakly supervised point cloud segmentation, i.e. semantically segmenting a
point cloud with only a few labeled points in the whole 3D scene, is highly
desirable due to the heavy burden of collecting abundant dense annotations for
the model training. However, existing methods remain challenging to accurately
segment 3D point clouds since limited annotated data may lead to insufficient
guidance for label propagation to unlabeled data. Considering the
smoothness-based methods have achieved promising progress, in this paper, we
advocate applying the consistency constraint under various perturbations to
effectively regularize unlabeled 3D points. Specifically, we propose a novel
DAT (\textbf{D}ual \textbf{A}daptive \textbf{T}ransformations) model for weakly
supervised point cloud segmentation, where the dual adaptive transformations
are performed via an adversarial strategy at both point-level and region-level,
aiming at enforcing the local and structural smoothness constraints on 3D point
clouds. We evaluate our proposed DAT model with two popular backbones on the
large-scale S3DIS and ScanNet-V2 datasets. Extensive experiments demonstrate
that our model can effectively leverage the unlabeled 3D points and achieve
significant performance gains on both datasets, setting new state-of-the-art
performance for weakly supervised point cloud segmentation.Comment: ECCV 202
Data efficient deep learning for medical image analysis: A survey
The rapid evolution of deep learning has significantly advanced the field of
medical image analysis. However, despite these achievements, the further
enhancement of deep learning models for medical image analysis faces a
significant challenge due to the scarcity of large, well-annotated datasets. To
address this issue, recent years have witnessed a growing emphasis on the
development of data-efficient deep learning methods. This paper conducts a
thorough review of data-efficient deep learning methods for medical image
analysis. To this end, we categorize these methods based on the level of
supervision they rely on, encompassing categories such as no supervision,
inexact supervision, incomplete supervision, inaccurate supervision, and only
limited supervision. We further divide these categories into finer
subcategories. For example, we categorize inexact supervision into multiple
instance learning and learning with weak annotations. Similarly, we categorize
incomplete supervision into semi-supervised learning, active learning, and
domain-adaptive learning and so on. Furthermore, we systematically summarize
commonly used datasets for data efficient deep learning in medical image
analysis and investigate future research directions to conclude this survey.Comment: Under Revie
Semi-Supervised and Unsupervised Deep Visual Learning: A Survey
State-of-the-art deep learning models are often trained with a large amountof costly labeled training data. However, requiring exhaustive manualannotations may degrade the model's generalizability in the limited-labelregime. Semi-supervised learning and unsupervised learning offer promisingparadigms to learn from an abundance of unlabeled visual data. Recent progressin these paradigms has indicated the strong benefits of leveraging unlabeleddata to improve model generalization and provide better model initialization.In this survey, we review the recent advanced deep learning algorithms onsemi-supervised learning (SSL) and unsupervised learning (UL) for visualrecognition from a unified perspective. To offer a holistic understanding ofthe state-of-the-art in these areas, we propose a unified taxonomy. Wecategorize existing representative SSL and UL with comprehensive and insightfulanalysis to highlight their design rationales in different learning scenariosand applications in different computer vision tasks. Lastly, we discuss theemerging trends and open challenges in SSL and UL to shed light on futurecritical research directions.<br
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