Papers Unsupervised Object Segmentation
“Unsupervised Object Segmentation” 태그가 달린 논문 40편 · 필터 해제
MUFASA: A Multi-Layer Framework for Slot Attention
Unsupervised object-centric learning (OCL) decomposes visual scenes into distinct entities. Slot attention is a popular approach that represents individual objects as latent vectors, called slots. Current methods obtain …
Unsupervised Object SegmentationObject Learning and Robust 3D Reconstruction
In this thesis we discuss architectural designs and training methods for a neural network to have the ability of dissecting an image into objects of interest without supervision. The main challenge in 2D unsupervised obj…
3D ReconstructionObjectSemantic SegmentationUnsupervised Object SegmentationAligning Instance-Semantic Sparse Representation towards Unsupervised Object Segmentation and Shape Abstraction with Repeatable Primitives
Understanding 3D object shapes necessitates shape representation by object parts abstracted from results of instance and semantic segmentation. Promising shape representations enable computers to interpret a shape with m…
Instance SegmentationObjectSemantic SegmentationUnsupervised Object SegmentationScaling White-Box Transformers for Vision
CRATE, a white-box transformer architecture designed to learn compressed and sparse representations, offers an intriguing alternative to standard vision transformers (ViTs) due to its inherent mathematical interpretabili…
Semantic SegmentationUnsupervised Object SegmentationMOD-UV: Learning Mobile Object Detectors from Unlabeled Videos
Embodied agents must detect and localize objects of interest, e.g. traffic participants for self-driving cars. Supervision in the form of bounding boxes for this task is extremely expensive. As such, prior work has looke…
Motion SegmentationObjectobject-detectionObject Detection+5Benchmarking and Analysis of Unsupervised Object Segmentation from Real-world Single Images
In this paper, we study the problem of unsupervised object segmentation from single images. We do not introduce a new algorithm, but systematically investigate the effectiveness of existing unsupervised models on challen…
BenchmarkingObjectSemantic SegmentationUnsupervised Object SegmentationSPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers
Unsupervised object-centric learning aims to decompose scenes into interpretable object entities, termed slots. Slot-based auto-encoders stand out as a prominent method for this task. Within them, crucial aspects include…
DecoderObjectSegmentationSemantic Segmentation+1SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models
Object-centric learning aims to represent visual data with a set of object entities (a.k.a. slots), providing structured representations that enable systematic generalization. Leveraging advanced architectures like Trans…
Image GenerationObjectObject DiscoverySemantic Segmentation+3Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping
We study learning object segmentation from unlabeled videos. Humans can easily segment moving objects without knowing what they are. The Gestalt law of common fate, i.e., what move at the same speed belong together, has …
Motion SegmentationObjectObject DiscoveryOptical Flow Estimation+6Probing neural representations of scene perception in a hippocampally dependent task using artificial neural networks
Deep artificial neural networks (DNNs) trained through backpropagation provide effective models of the mammalian visual system, accurately capturing the hierarchy of neural responses through primary visual cortex to infe…
HippocampusSemantic SegmentationTripletUnsupervised Object SegmentationDeepCut: Unsupervised Segmentation using Graph Neural Networks Clustering
Image segmentation is a fundamental task in computer vision. Data annotation for training supervised methods can be labor-intensive, motivating unsupervised methods. Current approaches often rely on extracting deep featu…
ClusteringGraph Neural NetworkImage SegmentationObject Localization+5ILSGAN: Independent Layer Synthesis for Unsupervised Foreground-Background Segmentation
Unsupervised foreground-background segmentation aims at extracting salient objects from cluttered backgrounds, where Generative Adversarial Network (GAN) approaches, especially layered GANs, show great promise. However, …
Generative Adversarial NetworkImage GenerationImage SegmentationSegmentation+2Unsupervised Multi-object Segmentation by Predicting Probable Motion Patterns
We propose a new approach to learn to segment multiple image objects without manual supervision. The method can extract objects form still images, but uses videos for supervision. While prior works have considered motion…
Image ReconstructionObjectSemantic SegmentationUnsupervised Object SegmentationPromising or Elusive? Unsupervised Object Segmentation from Real-world Single Images
In this paper, we study the problem of unsupervised object segmentation from single images. We do not introduce a new algorithm, but systematically investigate the effectiveness of existing unsupervised models on challen…
ObjectSemantic SegmentationUnsupervised Object SegmentationMotion-inductive Self-supervised Object Discovery in Videos
In this paper, we consider the task of unsupervised object discovery in videos. Previous works have shown promising results via processing optical flows to segment objects. However, taking flow as input brings about two …
ObjectObject DiscoveryObject Discovery In VideosOptical Flow Estimation+3A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation
We propose a simple, yet powerful approach for unsupervised object segmentation in videos. We introduce an objective function whose minimum represents the mask of the main salient object over the input sequence. It only …
Clusteringglobal-optimizationObjectSemantic Segmentation+5TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut
In this paper, we describe a graph-based algorithm that uses the features obtained by a self-supervised transformer to detect and segment salient objects in images and videos. With this approach, the image patches that c…
Object DiscoverySaliency DetectionSegmentationSemantic Segmentation+6Refine and Represent: Region-to-Object Representation Learning
Recent works in self-supervised learning have demonstrated strong performance on scene-level dense prediction tasks by pretraining with object-centric or region-based correspondence objectives. In this paper, we present …
Instance SegmentationObjectRepresentation LearningSegmentation+3Segmenting Moving Objects via an Object-Centric Layered Representation
The objective of this paper is a model that is able to discover, track and segment multiple moving objects in a video. We make four contributions: First, we introduce an object-centric segmentation model with a depth-ord…
Instance SegmentationMotion SegmentationObjectSegmentation+3Unsupervised Multi-object Segmentation Using Attention and Soft-argmax
We introduce a new architecture for unsupervised object-centric representation learning and multi-object detection and segmentation, which uses a translation-equivariant attention mechanism to predict the coordinates of …
Objectobject-detectionObject DetectionRepresentation Learning+3