T-Rex: Counting by Visual Prompting
Qing Jiang Feng Li Tianhe Ren Shilong Liu Zhaoyang Zeng Kent Yu Lei Zhang
We introduce T-Rex, an interactive object counting model designed to first
detect and then count any objects. We formulate object counting as an open-set
object detection task with the integration of visual prompts. Users can specify
the objects of interest by marking points or boxes on a reference image, and
T-Rex then detects all objects with a similar pattern. Guided by the visual
feedback from T-Rex, users can also interactively refine the counting results
by prompting on missing or falsely-detected objects. T-Rex has achieved
state-of-the-art performance on several class-agnostic counting benchmarks. To
further exploit its potential, we established a new counting benchmark
encompassing diverse scenarios and challenges. Both quantitative and
qualitative results show that T-Rex possesses exceptional zero-shot counting
capabilities. We also present various practical application scenarios for
T-Rex, illustrating its potential in the realm of visual prompting.