Papers Unsupervised Object Segmentation
“Unsupervised Object Segmentation” 태그가 달린 논문 40편 · 필터 해제
Guess 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+4EM-driven unsupervised learning for efficient motion segmentation
In this paper, we present a CNN-based fully unsupervised method for motion segmentation from optical flow. We assume that the input optical flow can be represented as a piecewise set of parametric motion models, typicall…
Data AugmentationMotion SegmentationOptical Flow EstimationSegmentation+1ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
There has been a recent surge in methods that aim to decompose and segment scenes into multiple objects in an unsupervised manner, i.e., unsupervised multi-object segmentation. Performing such a task is a long-standing g…
SegmentationSemantic SegmentationUnsupervised Object SegmentationThe Emergence of Objectness: Learning Zero-Shot Segmentation from Videos
Humans can easily segment moving objects without knowing what they are. That objectness could emerge from continuous visual observations motivates us to model grouping and movement concurrently from unlabeled videos. Our…
Contrastive LearningImage SegmentationSegmentationSemantic Segmentation+4Unsupervised Image Decomposition with Phase-Correlation Networks
The ability to decompose scenes into their object components is a desired property for autonomous agents, allowing them to reason and act in their surroundings. Recently, different methods have been proposed to learn obj…
ObjectObject DiscoveryUnsupervised Image DecompositionUnsupervised Object SegmentationGENESIS-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+4Self-supervised Video Object Segmentation by Motion Grouping
Animals have evolved highly functional visual systems to understand motion, assisting perception even under complex environments. In this paper, we work towards developing a computer vision system able to segment objects…
Motion SegmentationObjectOptical Flow EstimationSemantic Segmentation+4Learning Foreground-Background Segmentation from Improved Layered GANs
Deep learning approaches heavily rely on high-quality human supervision which is nonetheless expensive, time-consuming, and error-prone, especially for image segmentation task. In this paper, we propose a method to autom…
Generative Adversarial NetworkImage SegmentationSegmentationSemantic Segmentation+1Language-Mediated, Object-Centric Representation Learning
We present Language-mediated, Object-centric Representation Learning (LORL), a paradigm for learning disentangled, object-centric scene representations from vision and language. LORL builds upon recent advances in unsupe…
ObjectObject DiscoveryReferring ExpressionReferring Expression Comprehension+3Iterative Knowledge Exchange Between Deep Learning and Space-Time Spectral Clustering for Unsupervised Segmentation in Videos
We propose a dual system for unsupervised object segmentation in video, which brings together two modules with complementary properties: a space-time graph that discovers objects in videos and a deep network that learns …
ClusteringObjectObject DiscoverySemantic Segmentation+1DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping
We describe an unsupervised method to detect and segment portions of images of live scenes that, at some point in time, are seen moving as a coherent whole, which we refer to as objects. Our method first partitions the m…
Continual LearningObjectobject-detectionObject Detection+7Object Segmentation Without Labels with Large-Scale Generative Models
The recent rise of unsupervised and self-supervised learning has dramatically reduced the dependency on labeled data, providing effective image representations for transfer to downstream vision tasks. Furthermore, recent…
image-classificationImage ClassificationObjectSaliency Detection+4Unsupervised Object Segmentation with Explicit Localization Module
In this paper, we propose a novel architecture that iteratively discovers and segments out the objects of a scene based on the image reconstruction quality. Different from other approaches, our model uses an explicit loc…
Image ReconstructionObjectSemantic SegmentationUnsupervised Object SegmentationGENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
Generative latent-variable models are emerging as promising tools in robotics and reinforcement learning. Yet, even though tasks in these domains typically involve distinct objects, most state-of-the-art generative model…
Image GenerationObject DiscoveryReinforcement LearningRepresentation Learning+3Emergence of Object Segmentation in Perturbed Generative Models
We introduce a novel framework to build a model that can learn how to segment objects from a collection of images without any human annotation. Our method builds on the observation that the location of object segments ca…
ObjectSemantic SegmentationUnsupervised Object SegmentationObject Discovery with a Copy-Pasting GAN
We tackle the problem of object discovery, where objects are segmented for a given input image, and the system is trained without using any direct supervision whatsoever. A novel copy-pasting GAN framework is proposed, w…
ObjectObject DiscoveryUnsupervised Object SegmentationUnsupervised Object Segmentation by Redrawing
Object segmentation is a crucial problem that is usually solved by using supervised learning approaches over very large datasets composed of both images and corresponding object masks. Since the masks have to be provided…
ObjectSegmentationSemantic SegmentationUnsupervised Object SegmentationMulti-Object Representation Learning with Iterative Variational Inference
Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities. Yet most work on representation learning focuses on feature learning w…
ObjectRepresentation LearningSystematic GeneralizationUnsupervised Object Segmentation+1MONet: Unsupervised Scene Decomposition and Representation
The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence. Where those basic building blocks share meaningful properties, interactions and other regularities across scenes, …
Object DiscoveryUnsupervised Object SegmentationUnsupervised object segmentation in video by efficient selection of highly probable positive features
We address an essential problem in computer vision, that of unsupervised object segmentation in video, where a main object of interest in a video sequence should be automatically separated from its background. An efficie…
ObjectSemantic SegmentationUnsupervised Object Segmentation