Image Retrieval On Rparis Hard

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mAP

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이 벤치마크에서 각 모델의 성능 결과

		Paper Title
AMES	89.7	AMES: Asymmetric and Memory-Efficient Similarity Estimation for Instance-level Retrieval
SuperGlobal	86.7	Global Features are All You Need for Image Retrieval and Reranking
Hypergraph propagation	83.3	Hypergraph Propagation and Community Selection for Objects Retrieval
Token	78.56	Learning Token-based Representation for Image Retrieval
DELG+ α QE reranking + RRT reranking	77.7	Instance-level Image Retrieval using Reranking Transformers
ResNet101+ArcFace GLDv2-train-clean	70.3	Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval
FIRe	70.0	Learning Super-Features for Image Retrieval
DELF–HQE+SP	69.3	Large-Scale Image Retrieval with Attentive Deep Local Features
HOW	62.4	Learning and aggregating deep local descriptors for instance-level recognition
R–R-MAC	59.4	Particular object retrieval with integral max-pooling of CNN activations
R–GeM	56.3	Fine-tuning CNN Image Retrieval with No Human Annotation
DELF–ASMK*+SP	55.4	Large-Scale Image Retrieval with Attentive Deep Local Features
Dino	51.6	Emerging Properties in Self-Supervised Vision Transformers
R – [O] –CroW	47.2	Cross-dimensional Weighting for Aggregated Deep Convolutional Features
HesAff–rSIFT–HQE+SP	45.1	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
HesAff–rSIFT–HQE	44.7	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
R – [O] –SPoC	44.7	Aggregating Local Deep Features for Image Retrieval
R – [O] –MAC	44.1	Particular object retrieval with integral max-pooling of CNN activations
HesAff–rSIFT–ASMK*+SP	35.0	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
HesAff–rSIFT–ASMK*	34.5	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking

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Image Retrieval On Rparis Hard

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