Single-object discovery
8 papers with code • 5 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Single-object discovery
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MS COCO
PASCAL VOC
Object Discovery
Most implemented papers
Emerging Properties in Self-Supervised Vision Transformers
facebookresearch/dino
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ICCV 2021
In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets).
Localizing Objects with Self-Supervised Transformers and no Labels
valeoai/LOST
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We also show that training a class-agnostic detector on the discovered objects boosts results by another 7 points.
Unsupervised Image Matching and Object Discovery as Optimization
Learning with complete or partial supervision is powerful but relies on ever-growing human annotation efforts.
Toward unsupervised, multi-object discovery in large-scale image collections
This paper addresses the problem of discovering the objects present in a collection of images without any supervision.
Large-Scale Unsupervised Object Discovery
Extensive experiments on COCO and OpenImages show that, in the single-object discovery setting where a single prominent object is sought in each image, the proposed LOD (Large-scale Object Discovery) approach is on par with, or better than the state of the art for medium-scale datasets (up to 120K images), and over 37% better than the only other algorithms capable of scaling up to 1. 7M images.
Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut
YangtaoWANG95/TokenCut
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CVPR 2022
For unsupervised saliency detection, we improve IoU for 4. 9%, 5. 2%, 12. 9% on ECSSD, DUTS, DUT-OMRON respectively compared to previous state of the art.
MOVE: Unsupervised Movable Object Segmentation and Detection
We introduce MOVE, a novel method to segment objects without any form of supervision.
PEEKABOO: Hiding parts of an image for unsupervised object localization
Localizing objects in an unsupervised manner poses significant challenges due to the absence of key visual information such as the appearance, type and number of objects, as well as the lack of labeled object classes typically available in supervised settings.
