Unsupervised Object Detection

6 papers with code • 10 benchmarks • 9 datasets

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Most implemented papers

Cut and Learn for Unsupervised Object Detection and Instance Segmentation

facebookresearch/cutler 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 24 Jan 2020

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

shaohua0116/MultiDigitMNIST 3 Jun 2021

Recent studies on unsupervised object detection based on spatial attention have achieved promising results.

Class-aware Sounding Objects Localization via Audiovisual Correspondence

gewu-lab/csol_tpami2021 22 Dec 2021

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

nvlabs/freesolo CVPR 2022

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

sa4s-serc/adamls 19 Aug 2023

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.