This paper introduces NEMO, an approach to unsupervised object detection that uses motion---instead of image labels---as a cue to learn object detection. To discriminate between motion of the target object and other changes in the image, it relies on negative examples that show the scene without the object. The required data can be collected very easily by recording two short videos, a positive one showing the object in motion and a negative one showing the scene without the object. Without any additional form of pretraining or supervision and despite of occlusions, distractions, camera motion, and adverse lighting, those videos are sufficient to learn object detectors that can be applied to new videos and even generalize to unseen scenes and camera angles. In a baseline comparison, unsupervised object detection outperforms off-the shelf template matching and tracking approaches that are given an initial bounding box of the object. The learned object representations are also shown to be accurate enough to capture the relevant information from manipulation task demonstrations, which makes them applicable to learning from demonstration in robotics. An example of object detection that was learned from 3 minutes of video can be found here: http://y2u.be/u_jyz9_ETz4
Learning to detect objects without image labels from 3 minutes of video