Deep learning face attributes in the wild
Refereed conference paper presented and published in conference proceedings

替代計量分析
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其它資訊
摘要Predicting face attributes in the wild is challenging due to complex face variations. We propose a novel deep learning framework for attribute prediction in the wild. It cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently. LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction. This framework not only outperforms the state-of-the-art with a large margin, but also reveals valuable facts on learning face representation. (1) It shows how the performances of face localization (LNet) and attribute prediction (ANet) can be improved by different pre-training strategies. (2) It reveals that although the filters of LNet are fine-tuned only with image-level attribute tags, their response maps over entire images have strong indication of face locations. This fact enables training LNet for face localization with only image-level annotations, but without face bounding boxes or landmarks, which are required by all attribute recognition works. (3) It also demonstrates that the high-level hidden neurons of ANet automatically discover semantic concepts after pre-training with massive face identities, and such concepts are significantly enriched after fine-tuning with attribute tags. Each attribute can be well explained with a sparse linear combination of these concepts.
著者Liu Z., Luo P., Wang X., Tang X.
會議名稱15th IEEE International Conference on Computer Vision, ICCV 2015
會議開始日11.12.2015
會議完結日18.12.2015
會議地點Santiago
會議國家/地區智利共和國
詳細描述organized by IEEE,
出版年份2016
月份2
日期17
卷號11-18-December-2015
頁次3730 - 3738
國際標準書號9781467383912
國際標準期刊號1550-5499
語言英式英語

上次更新時間 2020-06-08 於 02:04