Detecting the unknown in Object Detection
- URL: http://arxiv.org/abs/2208.11641v1
- Date: Wed, 24 Aug 2022 16:27:38 GMT
- Title: Detecting the unknown in Object Detection
- Authors: Dario Fontanel, Matteo Tarantino, Fabio Cermelli, Barbara Caputo
- Abstract summary: We propose a novel training strategy, called UNKAD, able to predict unknown objects without requiring any annotation.
UNKAD first identifies and pseudo-labels unknown objects and then uses the pseudo-annotations to train an additional unknown class.
While UNKAD can directly detect unknown objects, we further combine it with previous unknown detection techniques, showing that it improves their performance at no costs.
- Score: 20.84221126313118
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Object detection methods have witnessed impressive improvements in the last
years thanks to the design of novel neural network architectures and the
availability of large scale datasets. However, current methods have a
significant limitation: they are able to detect only the classes observed
during training time, that are only a subset of all the classes that a detector
may encounter in the real world. Furthermore, the presence of unknown classes
is often not considered at training time, resulting in methods not even able to
detect that an unknown object is present in the image. In this work, we address
the problem of detecting unknown objects, known as open-set object detection.
We propose a novel training strategy, called UNKAD, able to predict unknown
objects without requiring any annotation of them, exploiting non annotated
objects that are already present in the background of training images. In
particular, exploiting the four-steps training strategy of Faster R-CNN, UNKAD
first identifies and pseudo-labels unknown objects and then uses the
pseudo-annotations to train an additional unknown class. While UNKAD can
directly detect unknown objects, we further combine it with previous unknown
detection techniques, showing that it improves their performance at no costs.
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