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. To
the best of our knowledge, we are the first to tackle the open-world counting
problem in this way. 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 is available at
this https URL

提出了 CounTX，一种基于 transformer decoder 的单阶段模型，可对任何类别的目标物体进行计数并能够针对使用文本描述任务的方法在 FSC-147 基准测试上超越现有技术。