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Cross Modal Retrieval With Noisy 2

评估指标

Image-to-text R@1

Image-to-text R@10

Image-to-text R@5

R-Sum

Text-to-image R@1

Text-to-image R@10

Text-to-image R@5

评测结果

各个模型在此基准测试上的表现结果

模型名称	Image-to-text R@1	Image-to-text R@10	Image-to-text R@5	R-Sum	Text-to-image R@1	Text-to-image R@10	Text-to-image R@5	Paper Title	Repository
CTPR-SGR	76.2	98.3	95.8	508.7	60.5	92.7	85.2	Learning From Noisy Correspondence With Tri-Partition for Cross-Modal Matching	-
CREAM	77.4	97.3	95.0	502.3	58.7	89.8	84.1	Cross-modal Retrieval with Noisy Correspondence via Consistency Refining and Mining
UGNCL	78.4	97.8	95.8	505.6	59.8	89.5	84.3	UGNCL: Uncertainty-Guided Noisy Correspondence Learning for Efficient Cross-Modal Matching
CRCL	77.9	98.3	95.4	507.8	60.9	90.6	84.7	Cross-modal Active Complementary Learning with Self-refining Correspondence	-
SREM	79.5	97.9	94.2	507.8	61.2	90.2	84.8	Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation	-
GSC-SGR	78.3	97.8	94.6	505.8	60.1	90.5	84.5	Mitigating Noisy Correspondence by Geometrical Structure Consistency Learning	-
MSCN	77.4	97.6	94.9	501.9	59.6	89.2	83.2	Noisy Correspondence Learning with Meta Similarity Correction	-
NAC	79.3	97.8	94.6	507.1	60.8	90.1	84.5	NAC: Mitigating Noisy Correspondence in Cross-Modal Matching Via Neighbor Auxiliary Corrector	-
L2RM-SGRAF	77.9	97.8	95.2	503.8	59.8	89.5	83.6	Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval	-
LNC	76.3	96.9	93.7	498.9	58.4	89.8	83.8	Learning with Noisy Correspondence	-
BiCro*	78.1	97.5	94.4	504.7	60.4	89.9	84.4	BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency	-
REPAIR	79.2	96.9	95.0	504.4	59.4	89.5	84.4	REPAIR: Rank Correlation and Noisy Pair Half-replacing with Memory for Noisy Correspondence	-
ReCon	80.3	97.8	95.3	511.8	61.6	91.3	85.5	ReCon: Enhancing True Correspondence Discrimination through Relation Consistency for Robust Noisy Correspondence Learning	-
NCR	75.0	97.5	93.9	496.7	58.3	89.0	83.0	Learning with Noisy Correspondence for Cross-modal Matching
DECL-SGRAF	77.5	97.0	93.8	494.7	56.1	88.5	81.8	Deep Evidential Learning with Noisy Correspondence for Cross-Modal Retrieval
RCL-SGR	74.2	96.9	91.8	487.2	55.6	87.5	81.2	Cross-Modal Retrieval with Partially Mismatched Pairs

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Cross Modal Retrieval With Noisy 2 | SOTA | HyperAI超神经