Unsupervised Object Segmentation
21 papers with code • 9 benchmarks • 11 datasets
Benchmarks
These leaderboards are used to track progress in Unsupervised Object Segmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Datasets
DUTS
DAVIS 2016
FBMS
SegTrack-v2
ECSSD
Multi-dSprites
ClevrTex
FBMS-59
Latest papers with no code
SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models
Finally, we demonstrate the scalability of SlotDiffusion to unconstrained real-world datasets such as PASCAL VOC and COCO, when integrated with self-supervised pre-trained image encoders.
Probing 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 inferior temporal cortex (IT).
DeepCut: Unsupervised Segmentation using Graph Neural Networks Clustering
This direct connection between the raw features and the clustering objective enables us to implicitly perform classification of the clusters between different graphs, resulting in part semantic segmentation without the need for additional post-processing steps.
Unsupervised Multi-object Segmentation by Predicting Probable Motion Patterns
We propose a new approach to learn to segment multiple image objects without manual supervision.
Motion-inductive Self-supervised Object Discovery in Videos
In this paper, we consider the task of unsupervised object discovery in videos.
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut
This method also achieves competitive results for unsupervised video object segmentation tasks with the DAVIS, SegTV2, and FBMS datasets.
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.
Self-supervised Video Object Segmentation by Motion Grouping
We additionally evaluate on a challenging camouflage dataset (MoCA), significantly outperforming the other self-supervised approaches, and comparing favourably to the top supervised approach, highlighting the importance of motion cues, and the potential bias towards visual appearance in existing video segmentation models.
Learning 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.
Language-Mediated, Object-Centric Representation Learning
These object-centric concepts derived from language facilitate the learning of object-centric representations.
