The Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results

2026-04-13

AI Summary

This work addresses the generalization challenge in cross-domain few-shot object detection (CD-FSOD), where target domains suffer from scarce annotations and significant distribution shifts relative to source domains. To systematically evaluate methods capable of achieving effective detection with only a few labeled samples in the target domain, we initiated and organized the NTIRE 2026 CD-FSOD Challenge. The competition featured both open- and closed-source tracks to foster community innovation and encourage diverse technical approaches that integrate few-shot learning, domain adaptation, and object detection. The challenge attracted 128 participants, yielding 696 submissions, with 19 teams delivering valid results. This effort substantially advanced the state of the art and expanded the performance boundaries of CD-FSOD, catalyzing further progress in the field.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Cross-domain few-shot object detection (CD-FSOD) remains a challenging problem for existing object detectors and few-shot learning approaches, particularly when generalizing across distinct domains. As part of NTIRE 2026, we hosted the second CD-FSOD Challenge to systematically evaluate and promote progress in detecting objects in unseen target domains under limited annotation conditions. The challenge received strong community interest, with 128 registered participants and a total of 696 submissions. Among them, 31 teams actively participated, and 19 teams submitted valid final results. Participants explored a wide range of strategies, introducing innovative methods that push the performance frontier under both open-source and closed-source tracks. This report presents a detailed overview of the NTIRE 2026 CD-FSOD Challenge, including a summary of the submitted approaches and an analysis of the final results across all participating teams. Challenge Codes: https://github.com/ohMargin/NTIRE2026_CDFSOD.

Problem

• Cross-domain
• Few-shot
• Object Detection
• Domain Generalization
• Limited Annotation

Innovation

• Cross-domain few-shot object detection
• NTIRE 2026
• domain generalization
• limited annotation
• object detection