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
Latest 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.
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos
rt219/the-emergence-of-objectness
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NeurIPS 2021
Our model starts with two separate pathways: an appearance pathway that outputs feature-based region segmentation for a single image, and a motion pathway that outputs motion features for a pair of images.
Unsupervised Image Decomposition with Phase-Correlation Networks
angelvillar96/Unsupervised-Decomposition-PCDNet-1
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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.
GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement
applied-ai-lab/genesis
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NeurIPS 2021
Moreover, object representations are often inferred using RNNs which do not scale well to large images or iterative refinement which avoids imposing an unnatural ordering on objects in an image but requires the a priori initialisation of a fixed number of object representations.
Object Segmentation Without Labels with Large-Scale Generative Models
anvoynov/BigGANsAreWatching
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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.
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
applied-ai-lab/genesis
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ICLR 2020
Generative latent-variable models are emerging as promising tools in robotics and reinforcement learning.
Emergence of Object Segmentation in Perturbed Generative Models
adambielski/perturbed-seg
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NeurIPS 2019
To force the generator to learn a representation where the foreground layer corresponds to an object, we perturb the output of the generative model by introducing a random shift of both the foreground image and mask relative to the background.
Unsupervised Object Segmentation by Redrawing
mickaelChen/ReDO
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NeurIPS 2019
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.
Object Discovery with a Copy-Pasting GAN
basilevh/object-discovery-cp-gan
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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.
Multi-Object Representation Learning with Iterative Variational Inference
deepmind/deepmind-research
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Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities.
