Introduces TSMa using text-visual channel interaction and SHARe using ViT layer-aligned autoregressive regression to improve prototype-based few-shot object detection, reporting +10.1 nAP on COCO.
Detect everything with few examples
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The NTIRE 2026 CD-FSOD Challenge report details innovative methods and performance results from 19 teams on cross-domain few-shot object detection in open- and closed-source tracks.
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Rethinking Prototype-based Similarity Learning for Few-Shot Object Detection
Introduces TSMa using text-visual channel interaction and SHARe using ViT layer-aligned autoregressive regression to improve prototype-based few-shot object detection, reporting +10.1 nAP on COCO.
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The Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results
The NTIRE 2026 CD-FSOD Challenge report details innovative methods and performance results from 19 teams on cross-domain few-shot object detection in open- and closed-source tracks.