HyperAI超神经
首页
资讯
最新论文
教程
数据集
百科
SOTA
LLM 模型天梯
GPU 天梯
顶会
开源项目
全站搜索
关于
中文
HyperAI超神经
Toggle sidebar
全站搜索…
⌘
K
首页
SOTA
Cross Modal Retrieval With Noisy
Cross Modal Retrieval With Noisy 2
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
Columns
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
0 of 16 row(s) selected.
Previous
Next