Tracking-based semi-supervised learning
International Journal of Robotics Research
Weakly supervised learning of object segmentations from web-scale video
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Iterative appearance learning with online multiple instance learning
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Efficiently Scaling up Crowdsourced Video Annotation
International Journal of Computer Vision
Scene transformation for detector adaptation
Pattern Recognition Letters
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We propose a new learning method which exploits temporal consistency to successfully learn a complex appearance model from a sparsely labeled training video. Our approach consists in iteratively improving an appearance-based model built with a Boosting procedure, and the reconstruction of trajectories corresponding to the motion of multiple targets. We demonstrate the efficiency of our procedure on pedestrian detection in videos and cell detection in microscopy image sequences. In both cases, our method is demonstrated to reduce the labeling requirement by one to two orders of magnitude. We show that in some instances, our method trained with sparse labels on a video sequence is able to outperform a standard learning procedure trained with the fully labeled sequence.