Unsupervised Object Localization
5 papers with code • 3 benchmarks • 2 datasets
Most implemented papers
Unsupervised Object Localization: Observing the Background to Discover Objects
This way, the salient objects emerge as a by-product without any strong assumption on what an object should be.
Optical Flow boosts Unsupervised Localization and Segmentation
Our fine-tuning procedure outperforms state-of-the-art techniques for unsupervised semantic segmentation through linear probing, without the use of any labeled data.
Unsupervised Object Localization with Representer Point Selection
We propose a novel unsupervised object localization method that allows us to explain the predictions of the model by utilizing self-supervised pre-trained models without additional finetuning.
CLIP-DIY: CLIP Dense Inference Yields Open-Vocabulary Semantic Segmentation For-Free
The emergence of CLIP has opened the way for open-world image perception.
Unsupervised Object Localization in the Era of Self-Supervised ViTs: A Survey
We propose here a survey of unsupervised object localization methods that discover objects in images without requiring any manual annotation in the era of self-supervised ViTs.