Image Retrieval On Rparis Medium

Metrics

mAP

Results

Performance results of various models on this benchmark

Model Name	mAP	Paper Title	Repository
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	-

0 of 23 row(s) selected.