  Object detection models perform well at localizing and classifying objects
that they are shown during training. However, due to the difficulty and cost
associated with creating and annotating detection datasets, trained models
detect a limited number of object types with unknown objects treated as
background content. This hinders the adoption of conventional detectors in
real-world applications like large-scale object matching, visual grounding,
visual relation prediction, obstacle detection (where it is more important to
determine the presence and location of objects than to find specific types),
etc. We propose class-agnostic object detection as a new problem that focuses
on detecting objects irrespective of their object-classes. Specifically, the
goal is to predict bounding boxes for all objects in an image but not their
object-classes. The predicted boxes can then be consumed by another system to
perform application-specific classification, retrieval, etc. We propose
training and evaluation protocols for benchmarking class-agnostic detectors to
advance future research in this domain. Finally, we propose (1) baseline
methods and (2) a new adversarial learning framework for class-agnostic
detection that forces the model to exclude class-specific information from
features used for predictions. Experimental results show that adversarial
learning improves class-agnostic detection efficacy.
