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Large Scale Semi-Supervised Object Detection Using Visual and Semantic Knowledge Transfer
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Deep CNN-based object detection systems have achieved remarkable success on several large-scale object detection benchmarks. However, training such detectors requires a large number of labeled bounding boxes, which are more difficult to obtain than image-level annotations. Previous work addresses this issue by transforming image-level classifiers into object detectors. This is done by modeling the differences between the two on categories with both imagelevel and bounding box annotations, and
doi:10.1109/cvpr.2016.233
dblp:conf/cvpr/TangWGDGC16
fatcat:bbz6v5uyw5d6zbssua5ki6lbwi