Revisiting knowledge transfer for training object class detectors

TL;DR

This paper introduces a unified framework for knowledge transfer in training object detectors using a semantic hierarchy, significantly improving performance over weakly supervised baselines and approaching fully supervised results.

Contribution

A novel multi-class object detector training method leveraging semantic hierarchy for broad knowledge transfer, outperforming previous transfer learning approaches.

Findings

Achieves 70.3% CorLoc and 36.9% mAP on target classes.

Reaches 80% of fully supervised detector performance.

Outperforms previous transfer learning results by significant margins.

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 (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.