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About Deep Convolutional Ranking For Miltilabel Image Annotation
This document proposes using a deep convolutional neural network with a ranking loss objective for multilabel image annotation. The network contains convolutional and dense layers like other CNNs. However, instead of classification losses, it uses a top-k ranking loss during training, which better fits the multilabel problem where images can have multiple tags. On the NUS-WIDE dataset, this approach outperforms conventional visual features by about 10%, achieving state-of-the-art performance for multilabel image annotation.
Detailed Information
| Author: | ['ap'] |
|---|---|
| Publication Year: | 2017 |
| Pages: | 9 |
| Language: | English |
| Format: | |
| Price: | FREE |
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