DeepBE: Learning Deep Binary Encoding for Multi-label Classification
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)(2016)
摘要
The track 2 and track 3 of ChaLearn 2016 can be considered as Multi-Label Classification problems. We present a framework of learning deep binary encoding (DeepBE) to deal with multi-label problems by transforming multi-labels to single labels. The transformation of DeepBE is in a hidden pattern, which can be well addressed by deep convolutions neural networks (CNNs). Furthermore, we adopt an ensemble strategy to enhance the learning robustness. This strategy is inspired by its effectiveness in fine-grained image recognition (FGIR) problem, while most of face related tasks such as track 2 and track 3 are also FGIR problems. By DeepBE, we got 5.45% and 10.84% mean square error for track 2 and track 3 respectively. Additionally, we proposed an algorithm adaption method to treat the multiple labels of track 2 directly and got 6.84% mean square error.
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关键词
learning deep binary encoding,DeepBE,multilabel classification problems,single labels,hidden pattern,deep convolutions neural networks,CNN,learning robustness,fine grained image recognition,FGIR problem,mean square error
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