16 research outputs found
Revisiting knowledge transfer for training object class detectors
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 (bicycle 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 across-dataset knowledge transfer
experiments [27, 24, 35] and find that (4) our technique outperforms the weakly
supervised baseline in all dataset pairs by 1.5x-1.9x, establishing its general
applicability.Comment: CVPR 1