Unsupervised Object Detection

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

10 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

12 shown of 12 papers with code (19 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Cut and Learn for Unsupervised Object Detection and Instance Segmentation
  26 Jan 2023 2 repositories listed
  We propose Cut-and-LEaRn (CutLER), a simple approach for training unsupervised object detection and segmentation models.
• Scene-Centric Unsupervised Panoptic Segmentation
  2 Apr 2025 1 repository listed Syntology ran 0 of 8 samples · 8 unverified
  Unsupervised panoptic segmentation aims to partition an image into semantically meaningful regions and distinct object instances without training on manually annotated data.
• Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels
  11 Mar 2025 1 repository listed Syntology ran 4 of 5 samples · 1 unverified · 5 pointer-only (licence)
  Subsequently, DOtA uses the complementary observations between agents to perform multi-scale encoding on preliminary labels, then decodes high-quality and low-quality labels.
• Vision-Language Guidance for LiDAR-based Unsupervised 3D Object Detection
  7 Aug 2024 1 repository listed
  To overcome these limitations, we propose a vision-language-guided unsupervised 3D detection approach that operates exclusively on LiDAR point clouds.
• Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene
  11 Jul 2024 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  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).
• UNION: Unsupervised 3D Object Detection using Object Appearance-based Pseudo-Classes
  24 May 2024 1 repository listed
  Subsequently, object proposals' visual appearances are encoded to distinguish static objects in the foreground and background by selecting static instances that are visually similar to dynamic objects.
• MOD-UV: Learning Mobile Object Detectors from Unlabeled Videos
  23 May 2024 1 repository listed Syntology ran 7 of 7 samples · 0 unverified
  As such, prior work has looked at unsupervised instance detection and segmentation, but in the absence of annotated boxes, it is unclear how pixels must be grouped into objects and which objects are of interest.
• Towards Self-Adaptive Machine Learning-Enabled Systems Through QoS-Aware Model Switching
  19 Aug 2023 1 repository listed
  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.
• FreeSOLO: Learning to Segment Objects without Annotations
  24 Feb 2022 1 repository listed
  FreeSOLO further demonstrates superiority as a strong pre-training method, outperforming state-of-the-art self-supervised pre-training methods by +9.
• Class-aware Sounding Objects Localization via Audiovisual Correspondence
  22 Dec 2021 1 repository listed Syntology ran 4 of 5 samples · 1 unverified · 3 pointer-only (licence)
  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.
• GMAIR: Unsupervised Object Detection Based on Spatial Attention and Gaussian Mixture
  3 Jun 2021 1 repository listed
  Recent studies on unsupervised object detection based on spatial attention have achieved promising results.
• An Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection
  24 Jan 2020 1 repository listed Syntology ran 0 of 18 samples · 18 unverified
  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.

Syntology lines on 6 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.