Image Retrieval On Rparis Medium

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mAP

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	mAP	Paper Title
HED-N-GAN	76.6	Dark Side Augmentation: Generating Diverse Night Examples for Metric Learning
HOW	81.6	Learning and aggregating deep local descriptors for instance-level recognition
FIRe	85.3	Learning Super-Features for Image Retrieval
R – [O] –CroW	70.4	Cross-dimensional Weighting for Aggregated Deep Convolutional Features
R–R-MAC	78.9	Particular object retrieval with integral max-pooling of CNN activations
HesAff–rSIFT–SMK*	59.0	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
Token	89.34	Learning Token-based Representation for Image Retrieval
DELF–ASMK*+SP	76.9	Large-Scale Image Retrieval with Attentive Deep Local Features
HesAff–rSIFT–ASMK*	61.2	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
DELF–HQE+SP	84.0	Large-Scale Image Retrieval with Attentive Deep Local Features
Dino	75.3	Emerging Properties in Self-Supervised Vision Transformers
HesAff–rSIFT–HQE+SP	70.2	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
DELG+ α QE reranking + RRT reranking	88.5	Instance-level Image Retrieval using Reranking Transformers
HesAff–rSIFT–SMK*+SP	59.2	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
R–GeM	77.2	Fine-tuning CNN Image Retrieval with No Human Annotation
ResNet101+ArcFace GLDv2-train-clean	84.9	Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval
HesAff–rSIFT–HQE	68.9	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
HesAff–rSIFT–VLAD	43.6	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
R – [O] –SPoC	69.2	Aggregating Deep Convolutional Features for Image Retrieval
R – [O] –MAC	66.2	Particular object retrieval with integral max-pooling of CNN activations

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