NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
Abstract
Domain fit: AI-adjacent · Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
Cross-Domain Few-Shot Object Detection (CD-FSOD) poses significant challenges to existing object detection and few-shot detection models when applied across domains. In conjunction with NTIRE 2025, we organized the 1st CDFSOD Challenge, aiming to advance the performance of current object detectors on entirely novel target domains with only limited labeled data. The challenge attracted 152 registered participants, received submissions from 42 teams, and concluded with 13 teams making valid final submissions. Participants approached the task from diverse perspectives, proposing novel models that achieved new state-of-the-art (SOTA) results under both open-source and closed-source settings. In this report, we present an overview of the 1st NTIRE 2025 CD-FSOD Challenge, highlighting the proposed solutions and summarizing the results submitted by the participants.
Results and benchmarks
Cross-Domain Few-Shot Object Detection (CD-FSOD) poses significant challenges to existing object detection and few-shot detection models when applied across domains.
Benchmark evidence is limited
Evidence graph: 3 refs, 3 links.
Utility signals: depth 65/100, grounding 75/100, status medium.
Implementation
No direct implementation yet
Maintained implementation evidence is not confirmed for this paper yet.
Use the implementation status and reproduction sections for the current action plan.
No verified maintained repo yet
There is no verified maintained implementation yet. Use this baseline plan to decide whether to prototype now or defer.
- No maintained paper-verified implementation was found; start with the closest related repositories below.
- Compare repo methods against the paper equations/algorithm before trusting metrics.
- Create a minimal baseline implementation from the paper and use adjacent repos as references.
Time to first repro: a few days
lovelyqian/NTIRE2025_CDFSOD is the closest maintained adjacent implementation (Matches contextual method/domain keyword: object detection). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 78 GitHub stars.
- Adjacent implementations are not paper-verified
- Recommended repository is adjacent and not paper-verified.
- Adjacent implementation match confidence is low.
Reproduction readiness
No repo
No verified implementation available
- No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.
Hardware requirements
- Expect multi-day setup/compute for meaningful reproduction based on current guidance.
Validation caveat
Framework baselines
- TorchVision object detection finetuning tutorial
Baseline setup for object detection workflows.
Repositories and ecosystem
Closest related implementations
These are not paper-verified. Use them as reference points when no direct implementation is available.
- lovelyqian/NTIRE2025_CDFSOD Adjacent · Confidence: Low · 78 stars
Matches contextual method/domain keyword: object detection
No additional verified repositories beyond the primary recommendation.
Hugging Face artifacts
No trustworthy direct or curated related Hugging Face artifacts were found yet. Use targeted searches to quickly locate candidate models, datasets, and demos.
Datasets
Spaces
Tip: start with models, then check datasets and spaces if you need evaluation data or demos.
Research context
30
Citations
59
References
Tasks
Computer science, Object (grammar), Task (project management), Conjunction (astronomy), Object detection, Detector, Task analysis, Key (lock)
Methods
None detected
Domains
Artificial intelligence, Computer vision, Machine learning, Physics and Astronomy
Open this paper in HFEPX to review benchmark signals, evaluation modes, and human-feedback protocol context.
Open in HFEPXJump to Paper2Code search queries derived from this paper's research context.