Papers Unsupervised Image Segmentation
“Unsupervised Image Segmentation” 태그가 달린 논문 61편 · 필터 해제
SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTA
Learning rich data representations from unlabeled data is a key challenge towards applying deep learning algorithms in downstream tasks. Several variants of variational autoencoders (VAEs) have been proposed to learn com…
Image GenerationImage ReconstructionImage SegmentationQuantization+3On-Device Unsupervised Image Segmentation
Along with the breakthrough of convolutional neural networks, learning-based segmentation has emerged in many research works. Most of them are based on supervised learning, requiring plenty of annotated data; however, to…
Image SegmentationSegmentationSemantic SegmentationUnsupervised Image SegmentationVariational multichannel multiclass segmentation using unsupervised lifting with CNNs
We propose an unsupervised image segmentation approach, that combines a variational energy functional and deep convolutional neural networks. The variational part is based on a recent multichannel multiphase Chan-Vese mo…
Image SegmentationSegmentationSemantic SegmentationUnsupervised Image SegmentationInverse Quantum Fourier Transform Inspired Algorithm for Unsupervised Image Segmentation
Image segmentation is a very popular and important task in computer vision. In this paper, inverse quantum Fourier transform (IQFT) for image segmentation has been explored and a novel IQFT-inspired algorithm is proposed…
Image SegmentationSegmentationSemantic SegmentationUnsupervised Image SegmentationImage Segmentation-based Unsupervised Multiple Objects Discovery
Unsupervised object discovery aims to localize objects in images, while removing the dependence on annotations required by most deep learning-based methods. To address this problem, we propose a fully unsupervised, botto…
Class-agnostic Object DetectionImage SegmentationObjectobject-detection+4Rethinking Alignment and Uniformity in Unsupervised Semantic Segmentation
Unsupervised image semantic segmentation(UISS) aims to match low-level visual features with semantic-level representations without outer supervision. In this paper, we address the critical properties from the view of fea…
Representation LearningSegmentationSemantic SegmentationUnsupervised Image Segmentation+1Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks
Unsupervised image segmentation is an important task in many real-world scenarios where labelled data is of scarce availability. In this paper we propose a novel approach that harnesses recent advances in unsupervised le…
Image SegmentationSegmentationSemantic SegmentationSuperpixels+2Improving Object-centric Learning with Query Optimization
The ability to decompose complex natural scenes into meaningful object-centric abstractions lies at the core of human perception and reasoning. In the recent culmination of unsupervised object-centric learning, the Slot-…
Image SegmentationObjectSemantic SegmentationUnsupervised Image Segmentation+1CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation
Segmenting medical images is critical to facilitating both patient diagnoses and quantitative research. A major limiting factor is the lack of labeled data, as obtaining expert annotations for each new set of imaging dat…
Contrastive LearningImage SegmentationMedical Image SegmentationSegmentation+4Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion
Motion, measured via optical flow, provides a powerful cue to discover and learn objects in images and videos. However, compared to using appearance, it has some blind spots, such as the fact that objects become invisibl…
Image SegmentationOptical Flow EstimationSegmentationSemantic Segmentation+4Self-Supervised Learning of Object Parts for Semantic Segmentation
Progress in self-supervised learning has brought strong general image representation learning methods. Yet so far, it has mostly focused on image-level learning. In turn, tasks such as unsupervised image segmentation hav…
Community DetectionImage SegmentationObjectRepresentation Learning+5Unsupervised Deep Learning Meets Chan-Vese Model
The Chan-Vese (CV) model is a classic region-based method in image segmentation. However, its piecewise constant assumption does not always hold for practical applications. Many improvements have been proposed but the is…
Deep LearningImage SegmentationmodelSegmentation+2Color Image Segmentation Using Multi-Objective Swarm Optimizer and Multi-level Histogram Thresholding
Rapid developments in swarm intelligence optimizers and computer processing abilities make opportunities to design more accurate, stable, and comprehensive methods for color image segmentation. This paper presents a new …
Image SegmentationSegmentationSemantic SegmentationUnsupervised Image SegmentationOne Stage Autoencoders for Multi-Domain Learning
Autoencoders (AEs) are widely being used for representation learning. Empirically AEs are capable of capturing hidden representations of a given domain precisely. However, in principle AEs’ latent representation might be…
ClusteringImage ClusteringImage SegmentationRepresentation Learning+2RAMA: A Rapid Multicut Algorithm on GPU
We propose a highly parallel primal-dual algorithm for the multicut (a.k.a. correlation clustering) problem, a classical graph clustering problem widely used in machine learning and computer vision. Our algorithm consist…
3D Instance SegmentationClusteringCombinatorial OptimizationGPU+5Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularization
Semantic segmentation is one of the basic, yet essential scene understanding tasks for an autonomous agent. The recent developments in supervised machine learning and neural networks have enjoyed great success in enhanci…
Image SegmentationScene UnderstandingSegmentationSemantic Segmentation+3GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement
Advances in unsupervised learning of object-representations have culminated in the development of a broad range of methods for unsupervised object segmentation and interpretable object-centric scene generation. These met…
ClusteringImage GenerationImage SegmentationObject+4Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep Learning
Hyperspectral stimulated Raman scattering (SRS) microscopy is a label-free technique for biomedical and mineralogical imaging which can suffer from low signal to noise ratios. Here we demonstrate the use of an unsupervis…
ClusteringDenoisingImage EnhancementImage Segmentation+3Deep Superpixel Cut for Unsupervised Image Segmentation
Image segmentation, one of the most critical vision tasks, has been studied for many years. Most of the early algorithms are unsupervised methods, which use hand-crafted features to divide the image into many regions. Re…
ClusteringImage SegmentationSegmentationSemantic Segmentation+2TricycleGAN: Unsupervised Image Synthesis and Segmentation Based on Shape Priors
Medical image segmentation is routinely performed to isolate regions of interest, such as organs and lesions. Currently, deep learning is the state of the art for automatic segmentation, but is usually limited by the nee…
Image GenerationImage SegmentationMedical Image SegmentationSegmentation+2