Single-object discovery
7 papers with code • 5 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Single-object discovery
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
MS COCO
PASCAL VOC
Object Discovery
Most implemented papers
Emerging Properties in Self-Supervised Vision Transformers
facebookresearch/dino
•
•
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
•
•
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
•
•
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.
