CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
DeepBE: Learning Deep Binary Encoding for Multi-Label Classification
Li, Chenghua1,2; Kang, Qi4; Ge, Guojing1; Song, Qiang1,2; Lu, Hanqing1,2; Cheng, Jian1,2,3
2016
Conference NameIEEE Conference on Computer Vision and Pattern Recognition Workshop
Conference Date2016.6.27-6.30
Conference PlaceLas Vegas, NV, USA
AbstractThe 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 multiplelabels of track 2 directly and got 6.84% mean square error.
KeywordChalearn2016 Deepbe Multi-label Classification
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20140
Collection模式识别国家重点实验室_图像与视频分析
Affiliation1.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.University of Chinese Academy of Sciences
3.Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences
4.Beijing Institute Of Technology
Recommended Citation
GB/T 7714
Li, Chenghua,Kang, Qi,Ge, Guojing,et al. DeepBE: Learning Deep Binary Encoding for Multi-Label Classification[C],2016.
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