Abstract
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 network multi-class object detector over all source classes, organized 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 generality, 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 manually
engineered objectness [11] (50.5% CorLoc, 25.4% mAP).
(2) delivers target object detectors reaching 80% of the
mAP of their fully supervised counterparts. (3) outperforms
the best reported transfer learning results on this dataset
(+41% CorLoc and +3% mAP over [18, 46], +16.2% mAP
over [32]). Moreover, we also carry out several acrossdataset knowledge transfer experiments [27, 24, 35] and
find that (4) our technique outperforms the weakly supervised baseline in all dataset pairs by 1.5 × ?1.9×, establishing its general applicability