International Journal of Computer Vision
Pictorial Structures for Object Recognition
International Journal of Computer Vision
A Discriminative Learning Framework with Pairwise Constraints for Video Object Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Level Grouping for Video Shots
International Journal of Computer Vision
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
FaceTracer: A Search Engine for Large Collections of Images with Faces
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Semi-Supervised Learning
Semi-supervised learning from a translation model between data distributions
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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In this work we investigate a weakly-supervised approach to learning facial attributes of humans in video. Given a small set of images labeled with attributes and a much larger unlabeled set of video tracks, we train a classifier to recognize these attributes in video data. We make two contributions. First, we show that training on video data improves classification performance over training on images alone. Second, and more significantly, we show that tracks in video provide a natural mechanism for generalizing training data --- in this case to new poses, lighting conditions and expressions. The advantage of our method is demonstrated on the classification of gender and age attributes in the movie "Love, Actually". We show that the semi-supervised approach adds a significant performance boost, for example for gender increasing average precision from 0.75 on static images alone to 0.85.