Improving Open-World Object Localization by Discovering Background
Journal:
arXiv
Published Date:
Apr 24, 2025
Abstract
Our work addresses the problem of learning to localize objects in an
open-world setting, i.e., given the bounding box information of a limited
number of object classes during training, the goal is to localize all objects,
belonging to both the training and unseen classes in an image, during
inference. Towards this end, recent work in this area has focused on improving
the characterization of objects either explicitly by proposing new objective
functions (localization quality) or implicitly using object-centric
auxiliary-information, such as depth information, pixel/region affinity map
etc. In this work, we address this problem by incorporating background
information to guide the learning of the notion of objectness. Specifically, we
propose a novel framework to discover background regions in an image and train
an object proposal network to not detect any objects in these regions. We
formulate the background discovery task as that of identifying image regions
that are not discriminative, i.e., those that are redundant and constitute low
information content. We conduct experiments on standard benchmarks to showcase
the effectiveness of our proposed approach and observe significant improvements
over the previous state-of-the-art approaches for this task.
