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Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations
Published on Oct 24, 20161931 Views
Multi-label learning has attracted significant interests in computer vision recently, finding applications in many vision tasks such as multiple object recognition and automatic image annotation. Asso
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Chapter list
Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations00:00
Multi-label Image Recognition IS Everywhere00:15
Dog ? 00:35
Dog, chair carpet, indoor, high five, pet, funny…00:39
Multi-label Image Recognition IS Everywhere/100:55
Multi-label Image Recognition IS Everywhere/201:06
Multi-label Image Recognition IS Everywhere/301:16
Multi-label Image Recognition IS Everywhere/401:20
Multi-label learning01:25
Challenges01:50
Missing Labels Problem/102:37
Missing Labels Problem/202:56
Missing Labels Problem/303:04
Missing Labels Problem/403:14
Missing Labels Problem/504:02
Missing Labels Problem/604:03
Label – Label correlations04:07
Instance – Instance Correlations04:32
We want to formulate the problem04:43
Intuition05:00
Semantic Feature Extraction/105:42
Semantic Feature Extraction/205:56
Semantic Feature Extraction/306:19
Local Semantic Descriptor/106:34
Local Semantic Descriptor/206:55
Local Semantic Descriptor/306:57
Graph Construction07:10
Experimental Results/108:43
System Architecture09:37
Experimental Results/209:41
Experimental Results/310:04