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Source Free Object Detection

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평가표: 2
보존된 평가 기록: 16

Source-Free Object Detection (SFOD) is a domain adaptation challenge in which only the pretrained source model weights are available during adaptation, with no access to the source data. The model must adapt solely using unlabeled samples from the target domain

코드

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

https://github.com/CuriousAI/mean-teacher

공식 코드

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

https://github.com/Lans1ng/SFOD-RS

공식 코드

CLIP-Guided Source-Free Object Detection in Aerial Images

https://github.com/Tajamul21/Grounded_Teacher

공식 코드

Context Aware Grounded Teacher for Source Free Object Detection

https://github.com/YXSong000/UAD

공식 코드

Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive Distillation

https://github.com/encounter1997/SFA

공식 코드

Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers

https://github.com/epfl-imos/simple-sfod

공식 코드

Simplifying Source-Free Domain Adaptation for Object Detection: Effective Self-Training Strategies and Performance Insights

https://github.com/kinredon/umt

공식 코드

Unbiased Mean Teacher for Cross-domain Object Detection

https://github.com/lbktrinh/DRU

공식 코드

Dynamic Retraining-Updating Mean Teacher for Source-Free Object Detection

https://github.com/vibashan/irg-sfda

공식 코드

Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection

https://github.com/Flashkong/Source-Free-Object-Detection-by-Learning-to-Overlook-Domain-Style

https://github.com/INK-USC/DualRE

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

https://github.com/Lan1991Xu/ONE_NeurIPS2018

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

https://github.com/RainbowNebula/PETS

Periodically Exchange Teacher-Student for Source-Free Object Detection

https://github.com/ZHKKKe/PixelSSL

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

https://github.com/benathi/fastswa-semi-sup

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

https://github.com/jxhuang0508/HCL

Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data

https://github.com/liuwei16/ALFNet

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

https://github.com/shunk031/chainer-MeanTeachers

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

https://github.com/sud0301/semisup-semseg

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

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