Unsupervised Object Segmentation
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Benchmarks
ClevrTex
DAVIS 2016
SegTrack-v2
FBMS-59
ObjectsRoom
ShapeStacks
ECSSD
DUTS
Most implemented
Multi-Object Representation Learning with Iterative Variational Inference
MONet: Unsupervised Scene Decomposition and Representation
Unsupervised Image Decomposition with Phase-Correlation Networks
GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
MOD-UV: Learning Mobile Object Detectors from Unlabeled Videos
Papers
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 Segmentation