HyperAI

Cross Modal Retrieval With Noisy 2

Métriques

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R-Sum
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Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
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Paper TitleRepository
CTPR-SGR76.298.395.8508.760.592.785.2Learning From Noisy Correspondence With Tri-Partition for Cross-Modal Matching-
CREAM77.497.395.0502.358.789.884.1Cross-modal Retrieval with Noisy Correspondence via Consistency Refining and Mining
UGNCL78.497.895.8505.659.889.584.3UGNCL: Uncertainty-Guided Noisy Correspondence Learning for Efficient Cross-Modal Matching
CRCL77.998.395.4507.860.990.684.7Cross-modal Active Complementary Learning with Self-refining Correspondence
SREM79.597.994.2507.861.290.284.8Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation-
GSC-SGR78.397.894.6505.860.190.584.5Mitigating Noisy Correspondence by Geometrical Structure Consistency Learning
MSCN77.497.694.9501.959.689.283.2Noisy Correspondence Learning with Meta Similarity Correction
NAC79.397.894.6507.160.890.184.5NAC: Mitigating Noisy Correspondence in Cross-Modal Matching Via Neighbor Auxiliary Corrector-
L2RM-SGRAF77.997.895.2503.859.889.583.6Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval
LNC76.396.993.7498.958.489.883.8Learning with Noisy Correspondence-
BiCro*78.197.594.4504.760.489.984.4BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency-
REPAIR79.296.995.0504.459.489.584.4REPAIR: Rank Correlation and Noisy Pair Half-replacing with Memory for Noisy Correspondence-
ReCon80.397.895.3511.861.691.385.5ReCon: Enhancing True Correspondence Discrimination through Relation Consistency for Robust Noisy Correspondence Learning
NCR75.097.593.9496.758.389.083.0Learning with Noisy Correspondence for Cross-modal Matching
DECL-SGRAF77.597.093.8494.756.188.581.8Deep Evidential Learning with Noisy Correspondence for Cross-Modal Retrieval
RCL-SGR74.296.991.8487.255.687.581.2Cross-Modal Retrieval with Partially Mismatched Pairs
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