Papers with Code 아카이브
Visual Place Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 40
- 보존된 평가 기록
- 265
Visual Place Recognition is the task of matching a view of a place with a different view of the same place taken at a different time.
Source: Visual place recognition using landmark distribution descriptors
Image credit: Visual place recognition using landmark distribution descriptors
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://gitlab.com/srrg-software/srrg_bench
공식 코드Adding Cues to Binary Feature Descriptors for Visual Place Recognition
https://gitlab.com/srrg-software/srrg_hbst
공식 코드HBST: A Hamming Distance embedding Binary Search Tree for Visual Place Recognition
https://gitlab.com/vail-uvm/geodtr
공식 코드Cross-view Geo-localization via Learning Disentangled Geometric Layout Correspondence
https://gitlab.com/vail-uvm/geodtr_plus
공식 코드GeoDTR+: Toward generic cross-view geolocalization via geometric disentanglement
https://gitlab.com/vail-uvm/seqgeo
공식 코드https://github.com/0xPr0xy/CoreML
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/0xPr0xy/MobileNet-CoreML
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/2023-MindSpore-1/ms-code-176
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/2023-MindSpore-4/Code12/tree/main/d2l/chapter_06_convolutional-neural-networks
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/2023-MindSpore-4/Code12/tree/main/d2l/chapter_07_convolutional-modern
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/2023-MindSpore-4/Code9/tree/main/MobileNet
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/dinov2
https://github.com/2024-MindSpore-1/Code4/tree/main/MobileNet/mobilenetv1
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/82magnolia/panoramic-localization
https://github.com/Adelaide-AI-Group/G2D
https://github.com/Adelaide-AI-Group/MCVL
Visual Localization Under Appearance Change: Filtering Approaches
https://github.com/AndresPMD/Clip_CMR
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/AwaleSajil/SuperGlue_Demo_Modified1
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/BIG-CHENG/FaceRec
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/BR-IDL/PaddleViT/tree/develop/self_supervised_learning/dino
https://github.com/BillyGun27/mobilenet_model
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/BingkAI-B21CAP0161/C-Mask-Machine-Learning
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/BurguerJohn/global_perceptual_similarity_loss
https://github.com/BurguerJohn/torch-felix
https://github.com/ByungKwanLee/Causal-Unsupervised-Segmentation
https://github.com/ChienYiChi/kaggle-panda-challenge
NetVLAD: CNN architecture for weakly supervised place recognition
https://github.com/DCASE-REPO/dcase2019_task2_baseline
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/DT42/BerryNet
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/Deci-AI/super-gradients
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/Edmonton-School-of-AI/ml5-Simple-Image-Classification
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/Expedit-LargeScale-Vision-Transformer/Expedit-DINO
https://github.com/FraCorti/Deep_Subnetworks_for_Dynamic_Resource_Constraints
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/FreddeFrallan/Multilingual-CLIP
Learning Transferable Visual Models From Natural Language Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.