Image Retrieval On Roxford Hard

Métriques

mAP

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	mAP	Paper Title
DELF–ASMK*+SP	43.1	Large-Scale Image Retrieval with Attentive Deep Local Features
R – [O] –MAC	18.0	Particular object retrieval with integral max-pooling of CNN activations
DELG+ α QE reranking+ RRT reranking	64	Instance-level Image Retrieval using Reranking Transformers
Hypergraph propagation+community selection	73	Hypergraph Propagation and Community Selection for Objects Retrieval
Token	66.57	Learning Token-based Representation for Image Retrieval
HesAff–rSIFT–VLAD	13.2	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
HesAff–rSIFT–SMK*+SP	35.8	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
DELF–HQE+SP	50.3	Large-Scale Image Retrieval with Attentive Deep Local Features
R–GeM	38.5	Fine-tuning CNN Image Retrieval with No Human Annotation
HesAff–rSIFT–ASMK*	36.4	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
Dino	24.3	Emerging Properties in Self-Supervised Vision Transformers
FIRe	61.2	Learning Super-Features for Image Retrieval
HesAff–rSIFT–HQE+SP	49.7	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
HesAff–rSIFT–ASMK*+SP	36.7	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
ResNet101+ArcFace GLDv2-train-clean	51.6	Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval
R–R-MAC	32.4	Particular object retrieval with integral max-pooling of CNN activations
HesAff–rSIFT–SMK*	35.4	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
HOW	56.9	Learning and aggregating deep local descriptors for instance-level recognition
R – [O] –CroW	13.3	Cross-dimensional Weighting for Aggregated Deep Convolutional Features
HesAff–rSIFT–HQE	41.3	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking

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