Source Free Object Detection

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

2 leaderboard tables shown for this task, 2 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

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

2 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.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

12 shown of 12 papers with code (17 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.

• Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
  6 Mar 2017 8 repositories listed Syntology ran 6 of 6 samples · 0 unverified · 6 pointer-only (licence)
  Without changing the network architecture, Mean Teacher achieves an error rate of 4.
• Context Aware Grounded Teacher for Source Free Object Detection
  21 Apr 2025 1 repository listed
  To tackle the problem of context bias and the significant performance drop of the student model in the SFOD setting, we introduce Grounded Teacher (GT) as a standard framework.
• Dynamic Retraining-Updating Mean Teacher for Source-Free Object Detection
  23 Jul 2024 1 repository listed
  In object detection, unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain.
• Simplifying Source-Free Domain Adaptation for Object Detection: Effective Self-Training Strategies and Performance Insights
  10 Jul 2024 1 repository listed
  This paper focuses on source-free domain adaptation for object detection in computer vision.
• Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive Distillation
  9 Feb 2024 1 repository listed
  Source-free domain adaptation (SFDA) alleviates the domain discrepancy among data obtained from domains without accessing the data for the awareness of data privacy.
• CLIP-Guided Source-Free Object Detection in Aerial Images
  10 Jan 2024 1 repository listed
  Domain adaptation is crucial in aerial imagery, as the visual representation of these images can significantly vary based on factors such as geographic location, time, and weather conditions.
• Periodically Exchange Teacher-Student for Source-Free Object Detection
  23 Nov 2023 1 repository listed
  To address this issue, we propose the Periodically Exchange Teacher-Student (PETS) method, a simple yet novel approach that introduces a multiple-teacher framework consisting of a static teacher, a dynamic teacher, and…
• Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection
  29 Mar 2022 1 repository listed
  The Source-Free Domain Adaptation (SFDA) setting aims to alleviate these concerns by adapting a source-trained model for the target domain without requiring access to the source data.
• Source-Free Object Detection by Learning To Overlook Domain Style
  1 Jan 2022 1 repository listed
  This approach suffers from both unsatisfactory accuracy of pseudo labels due to the presence of domain shift and limited use of target domain training data.
• Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data
  7 Oct 2021 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  To this end, we design an innovative historical contrastive learning (HCL) technique that exploits historical source hypothesis to make up for the absence of source data in UMA.
• Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers
  27 Jul 2021 1 repository listed
  In DQFA, a novel domain query is used to aggregate and align global context from the token sequence of both domains.
• Unbiased Mean Teacher for Cross-domain Object Detection
  2 Mar 2020 1 repository listed Syntology ran 2 of 6 samples · 4 unverified
  We reveal that there often exists a considerable model bias for the simple mean teacher (MT) model in cross-domain scenarios, and eliminate the model bias with several simple yet highly effective strategies.

Syntology lines on 3 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.