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Revisiting knowledge transfer for training object class detectors

Vittorio Ferrari
CVPR (2018) (to appear)


We propose to revisit knowledge transfer for training object detectors on target classes from weakly supervised training images, helped by a set of source classes with bounding-box annotations. We present a unified knowledge transfer framework based on training a single neural net- work multi-class object detector over all source classes, or- ganized in a semantic hierarchy. This generates proposals with scores at multiple levels in the hierarchy, which we use to explore knowledge transfer over a broad range of gen- erality, ranging from class-specific (bycicle to motorbike) to class-generic (objectness to any class). Experiments on the 200 object classes in the ILSVRC 2013 detection dataset show that our technique (1) leads to much better performance on the target classes (70.3% CorLoc, 36.9% mAP) than a weakly supervised baseline which uses man- ually engineered objectness [10] (50.5% CorLoc, 25.4% mAP). (2) delivers target object detectors reaching 80% of the mAP of their fully supervised counterparts. (3) outper- forms the best reported transfer learning results [17, 42] on this dataset (+41% CorLoc, +3% mAP). Moreover, we also carry out several across-dataset knowledge transfer exper- iments [25, 22, 32] and find that (4) our technique outper- forms the weakly supervised baseline in all dataset pairs by 1.5 × −1.9×, establishing its general applicability.

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