Papers with Code 아카이브

Single-object discovery

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

0

평가표

5

보존된 평가 기록

19

코드

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

https://github.com/adambielski/move-seg

공식 코드

MOVE: Unsupervised Movable Object Segmentation and Detection

https://github.com/facebookresearch/dino

공식 코드

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/hasibzunair/peekaboo

공식 코드

PEEKABOO: Hiding parts of an image for unsupervised object localization

https://github.com/huyvvo/LOD

공식 코드

Large-Scale Unsupervised Object Discovery

https://github.com/huyvvo/OSD

공식 코드

Unsupervised Image Matching and Object Discovery as Optimization

https://github.com/huyvvo/rOSD

공식 코드

Toward unsupervised, multi-object discovery in large-scale image collections

https://github.com/valeoai/LOST

공식 코드

Localizing Objects with Self-Supervised Transformers and no Labels

https://github.com/BR-IDL/PaddleViT/tree/develop/self_supervised_learning/dino

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/Expedit-LargeScale-Vision-Transformer/Expedit-DINO

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/MindSpore-scientific/code-7/tree/main/dino

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/OML-Team/open-metric-learning

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/PaddlePaddle/PASSL

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/TRAILab/ST-SLidR

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/YangtaoWANG95/TokenCut

Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut

https://github.com/adrienangeli/dino

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/ahmedelmahy/myownvit

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/alibaba/EasyCV

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/alijavidani/local_global_representation_learning

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/beresandras/contrastive-classification-keras

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/clemsgrs/hipt

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/computationalpathologygroup/hvit

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/facebookresearch/vissl

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/ipmi-icns-uke/sparsam

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/jankrepl/mildlyoverfitted

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/jmnolte/hccnet

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/kaiko-ai/eva

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/lightly-ai/lightly

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/lukemelas/deep-spectral-segmentation

Localizing Objects with Self-Supervised Transformers and no Labels

https://github.com/manantomar/video-occupancy-models

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/rajatkoner08/oodformer

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/sithu31296/simple-object-tracking

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/ttt496/vit-pytorch

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/valeoai/found

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/vturrisi/solo-learn

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/waltersimoncini/fungivision

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/woctezuma/steam-DINO

Emerging Properties in Self-Supervised Vision Transformers

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