Unsupervised Object Detection
8 papers with code • 10 benchmarks • 10 datasets
Benchmarks
These leaderboards are used to track progress in Unsupervised Object Detection
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Datasets
KITTI
LVIS
Objects365
PASCAL VOC 2007
Clipart1k
Watercolor2k
Comic2k
UVO
LeukemiaAttri
Most implemented papers
Cut and Learn for Unsupervised Object Detection and Instance Segmentation
facebookresearch/cutler
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CVPR 2023
We propose Cut-and-LEaRn (CutLER), a simple approach for training unsupervised object detection and segmentation models.
An Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection
51616/split-vae
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In this work, we propose a framework, called SPLIT, which allows us to disentangle local and global information into two separate sets of latent variables within the variational autoencoder (VAE) framework.
GMAIR: Unsupervised Object Detection Based on Spatial Attention and Gaussian Mixture
Recent studies on unsupervised object detection based on spatial attention have achieved promising results.
Class-aware Sounding Objects Localization via Audiovisual Correspondence
To address this problem, we propose a two-stage step-by-step learning framework to localize and recognize sounding objects in complex audiovisual scenarios using only the correspondence between audio and vision.
FreeSOLO: Learning to Segment Objects without Annotations
FreeSOLO further demonstrates superiority as a strong pre-training method, outperforming state-of-the-art self-supervised pre-training methods by +9. 8% AP when fine-tuning instance segmentation with only 5% COCO masks.
Towards Self-Adaptive Machine Learning-Enabled Systems Through QoS-Aware Model Switching
As a solution, we propose the concept of a Machine Learning Model Balancer, focusing on managing uncertainties related to ML models by using multiple models.
Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene
In this paper, we are among the early attempts to integrate LiDAR data with 2D images for unsupervised 3D detection and introduce a new method, dubbed LiDAR-2D Self-paced Learning (LiSe).
Vision-Language Guidance for LiDAR-based Unsupervised 3D Object Detection
To overcome these limitations, we propose a vision-language-guided unsupervised 3D detection approach that operates exclusively on LiDAR point clouds.
