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.

介绍了一种新方法，利用深度卷积神经网络对整张图片进行训练，以在不额外进行人工监督的情况下定位图像中的物体，并将该思想嵌入凝聚聚类技术中，产生自学习定位假设。实验证明，使用我们的方法自动生成的注释可用于训练物体探测器，产生接近于手动注释的边界框的识别结果。