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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/1811.06295 |
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| _version_ | 1866929604266033152 |
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| author | Du, Chen Wang, Chunheng Wang, Yanna Shi, Cunzhao Xiao, Baihua |
| author_facet | Du, Chen Wang, Chunheng Wang, Yanna Shi, Cunzhao Xiao, Baihua |
| contents | Different layers of deep convolutional neural networks(CNNs) can encode different-level information. High-layer features always contain more semantic information, and low-layer features contain more detail information. However, low-layer features suffer from the background clutter and semantic ambiguity. During visual recognition, the feature combination of the low-layer and high-level features plays an important role in context modulation. If directly combining the high-layer and low-layer features, the background clutter and semantic ambiguity may be caused due to the introduction of detailed information. In this paper, we propose a general network architecture to concatenate CNN features of different layers in a simple and effective way, called Selective Feature Connection Mechanism (SFCM). Low-level features are selectively linked to high-level features with a feature selector which is generated by high-level features. The proposed connection mechanism can effectively overcome the above-mentioned drawbacks. We demonstrate the effectiveness, superiority, and universal applicability of this method on multiple challenging computer vision tasks, including image classification, scene text detection, and image-to-image translation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1811_06295 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | Selective Feature Connection Mechanism: Concatenating Multi-layer CNN Features with a Feature Selector Du, Chen Wang, Chunheng Wang, Yanna Shi, Cunzhao Xiao, Baihua Computer Vision and Pattern Recognition Different layers of deep convolutional neural networks(CNNs) can encode different-level information. High-layer features always contain more semantic information, and low-layer features contain more detail information. However, low-layer features suffer from the background clutter and semantic ambiguity. During visual recognition, the feature combination of the low-layer and high-level features plays an important role in context modulation. If directly combining the high-layer and low-layer features, the background clutter and semantic ambiguity may be caused due to the introduction of detailed information. In this paper, we propose a general network architecture to concatenate CNN features of different layers in a simple and effective way, called Selective Feature Connection Mechanism (SFCM). Low-level features are selectively linked to high-level features with a feature selector which is generated by high-level features. The proposed connection mechanism can effectively overcome the above-mentioned drawbacks. We demonstrate the effectiveness, superiority, and universal applicability of this method on multiple challenging computer vision tasks, including image classification, scene text detection, and image-to-image translation. |
| title | Selective Feature Connection Mechanism: Concatenating Multi-layer CNN Features with a Feature Selector |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/1811.06295 |