Unsupervised Object Segmentation
21 papers with code • 9 benchmarks • 11 datasets
Benchmarks
These leaderboards are used to track progress in Unsupervised Object Segmentation
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Datasets
DUTS
DAVIS 2016
FBMS
SegTrack-v2
ECSSD
Multi-dSprites
ClevrTex
FBMS-59
Most implemented papers
ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
karazijal/clevrtex-generation
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We benchmark a large set of recent unsupervised multi-object segmentation models on ClevrTex and find all state-of-the-art approaches fail to learn good representations in the textured setting, despite impressive performance on simpler data.
EM-driven unsupervised learning for efficient motion segmentation
Etienne-Meunier-Inria/EM-Flow-Segmentation
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The core idea of our work is to leverage the Expectation-Maximization (EM) framework in order to design in a well-founded manner a loss function and a training procedure of our motion segmentation neural network that does not require either ground-truth or manual annotation.
Unsupervised 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 the objects present in the scene and to associate a feature vector to each object.
Segmenting 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.
Refine 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.
A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation
We propose a simple, yet powerful approach for unsupervised object segmentation in videos.
Promising or Elusive? Unsupervised Object Segmentation from Real-world Single Images
We firstly introduce four complexity factors to quantitatively measure the distributions of object- and scene-level biases in appearance and geometry for datasets with human annotations.
ILSGAN: Independent Layer Synthesis for Unsupervised Foreground-Background Segmentation
qrzou/ILSGAN
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Unsupervised foreground-background segmentation aims at extracting salient objects from cluttered backgrounds, where Generative Adversarial Network (GAN) approaches, especially layered GANs, show great promise.
Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping
TonyLianLong/RCF-UnsupVideoSeg
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CVPR 2023
The Gestalt law of common fate, i. e., what move at the same speed belong together, has inspired unsupervised object discovery based on motion segmentation.
SPOT: 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.
