  Our objective is open-world object counting in images, where the target
object class is specified by a text description. To this end, we propose
CounTX, a class-agnostic, single-stage model using a transformer decoder
counting head on top of pre-trained joint text-image representations. CounTX is
able to count the number of instances of any class given only an image and a
text description of the target object class, and can be trained end-to-end. In
addition to this model, we make the following contributions: (i) we compare the
performance of CounTX to prior work on open-world object counting, and show
that our approach exceeds the state of the art on all measures on the FSC-147
benchmark for methods that use text to specify the task; (ii) we present and
release FSC-147-D, an enhanced version of FSC-147 with text descriptions, so
that object classes can be described with more detailed language than their
simple class names. FSC-147-D and the code are available at
https://www.robots.ox.ac.uk/~vgg/research/countx.
