Self-taught Object Localization with Deep Networks

Self-taught Object Localization with Deep Networks
L. Bazzani, A. Bergamo, D. Anguelov, L. Torresani
In IEEE Winter Conference on Applications of Computer Vision (WACV), 2016
STL code / arXiv / bibtex
@conference{Bazzani:WACV16
  title     = {Self-taught Object Localization with Deep Networks},
  author    = {Bazzani, L. and Bergamo A. and Anguelov, D. and
               Torresani, L.},
  booktitle   = {In IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year      = {2016}
}
  
We leverage deep convolutional networks trained for whole-image recognition to localize objects in images without additional human supervision.

Details

This paper introduces self-taught object localization, a novel approach that leverages deep convolutional networks trained for whole-image recognition to localize objects in images without additional human supervision, i.e., without using any ground-truth bounding boxes for training. The key idea is to analyze the change in the recognition scores when artificially masking out different regions of the image. The masking out of a region that includes the object typically causes a significant drop in recognition score. This idea is embedded into an agglomerative clustering technique that generates self-taught localization hypotheses. Our object localization scheme outperforms existing proposal methods in both precision and recall for small number of subwindow proposals (e.g., on ILSVRC-2012 it produces a relative gain of 23.4% over the state-of-the-art for top-1 hypothesis). Furthermore, our experiments show that the annotations automatically-generated by our method can be used to train object detectors yielding recognition results remarkably close to those obtained by training on manually-annotated bounding boxes.

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