Papers with Code 아카이브

Cross-Domain Few-Shot Object Detection

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평가표: 6
보존된 평가 기록: 77

The task of Cross-Domain Few-Shot Object Detection (CD-FSOD) aims at tackling FSOD across domains.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/LONGXUANX/CDFormer_code

공식 코드

CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusion

https://github.com/MegviiDetection/FSCE

공식 코드

FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding

https://github.com/er-muyue/defrcn

공식 코드

DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection

https://github.com/facebookresearch/Detic

공식 코드

Detecting Twenty-thousand Classes using Image-level Supervision

https://github.com/facebookresearch/detectron2/tree/main/projects/ViTDet

공식 코드

Exploring Plain Vision Transformer Backbones for Object Detection

https://github.com/fsod/cd-fsod

공식 코드

CD-FSOD: A Benchmark for Cross-domain Few-shot Object Detection

https://github.com/hlings/acrofod

공식 코드

AcroFOD: An Adaptive Method for Cross-domain Few-shot Object Detection

https://github.com/jaychempan/ETS

공식 코드

Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object Detection

https://github.com/johnmaijer/x-few-_cd-fsod

공식 코드

NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results

https://github.com/lovelyqian/CDFSOD-benchmark

공식 코드

Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector

https://github.com/lovelyqian/ntire2025_cdfsod

공식 코드

NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results

https://github.com/miquel-espinosa/no-time-to-train

공식 코드

No time to train! Training-Free Reference-Based Instance Segmentation

https://github.com/mlzxy/devit

공식 코드

Detect Everything with Few Examples

https://github.com/pumpkinder/glip-cdfsod

공식 코드

NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results

https://github.com/ucbdrive/few-shot-object-detection

공식 코드

Frustratingly Simple Few-Shot Object Detection

https://github.com/MindCode-4/code-1/tree/main/vit

Exploring Plain Vision Transformer Backbones for Object Detection

https://github.com/MindCode-4/code-5/tree/main/vitdet

Exploring Plain Vision Transformer Backbones for Object Detection

https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/vitdet

Exploring Plain Vision Transformer Backbones for Object Detection

https://github.com/ViTAE-Transformer/ViTDet

Exploring Plain Vision Transformer Backbones for Object Detection

https://github.com/alibaba/EasyCV

Exploring Plain Vision Transformer Backbones for Object Detection

https://github.com/fsna2022/fsna

Frustratingly Simple Few-Shot Object Detection

https://github.com/hula-ai/DAMA

Exploring Plain Vision Transformer Backbones for Object Detection

https://github.com/kdexd/coco-rem

Exploring Plain Vision Transformer Backbones for Object Detection

https://github.com/lexisnexis-risk-open-source/ledetection

Frustratingly Simple Few-Shot Object Detection

https://github.com/megvii-research/fsce

Frustratingly Simple Few-Shot Object Detection

https://github.com/miquel-espinosa/samantics

No time to train! Training-Free Reference-Based Instance Segmentation

https://github.com/nvakhoa/mpad

DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection

https://github.com/pwc-1/Paper-9/tree/main/1/vitdet

Exploring Plain Vision Transformer Backbones for Object Detection

https://github.com/vitae-transformer/qformer

Exploring Plain Vision Transformer Backbones for Object Detection

https://gitlab.com/birder/birder

Exploring Plain Vision Transformer Backbones for Object Detection

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.