Image Retrieval On Roxford Hard

Metrics

mAP

Results

Performance results of various models on this benchmark

		Paper Title	Repository
SuperGlobal	80.2	Global Features are All You Need for Image Retrieval and Reranking
AMES	80	AMES: Asymmetric and Memory-Efficient Similarity Estimation for Instance-level Retrieval
Hypergraph propagation+community selection	73	Hypergraph Propagation and Community Selection for Objects Retrieval	-
Token	66.57	Learning Token-based Representation for Image Retrieval
DELG+ α QE reranking+ RRT reranking	64	Instance-level Image Retrieval using Reranking Transformers
FIRe	61.2	Learning Super-Features for Image Retrieval
HOW	56.9	Learning and aggregating deep local descriptors for instance-level recognition
ResNet101+ArcFace GLDv2-train-clean	51.6	Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval
DELF–HQE+SP	50.3	Large-Scale Image Retrieval with Attentive Deep Local Features
HesAff–rSIFT–HQE+SP	49.7	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
DELF–ASMK*+SP	43.1	Large-Scale Image Retrieval with Attentive Deep Local Features
HesAff–rSIFT–HQE	41.3	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
R–GeM	38.5	Fine-tuning CNN Image Retrieval with No Human Annotation
HesAff–rSIFT–ASMK*+SP	36.7	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
HesAff–rSIFT–ASMK*	36.4	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
HesAff–rSIFT–SMK*+SP	35.8	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
HesAff–rSIFT–SMK*	35.4	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
R–R-MAC	32.4	Particular object retrieval with integral max-pooling of CNN activations
Dino	24.3	Emerging Properties in Self-Supervised Vision Transformers
R – [O] –MAC	18.0	Particular object retrieval with integral max-pooling of CNN activations

0 of 23 row(s) selected.

Command Palette

Image Retrieval On Roxford Hard

Metrics

Results