Self-taught object localization with deep networks

L Bazzani, A Bergamo, D Anguelov… - 2016 IEEE winter …, 2016 - ieeexplore.ieee.org
2016 IEEE winter conference on applications of computer vision (WACV), 2016ieeexplore.ieee.org
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, ie, 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 …
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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