  Vision foundation models have been explored recently to build general-purpose
vision systems. However, predominant paradigms, driven by casting
instance-level tasks as an object-word alignment, bring heavy cross-modality
interaction, which is not effective in prompting object detection and visual
grounding. Another line of work that focuses on pixel-level tasks often
encounters a large annotation gap of things and stuff, and suffers from mutual
interference between foreground-object and background-class segmentation. In
stark contrast to the prevailing methods, we present APE, a universal visual
perception model for aligning and prompting everything all at once in an image
to perform diverse tasks, i.e., detection, segmentation, and grounding, as an
instance-level sentence-object matching paradigm. Specifically, APE advances
the convergence of detection and grounding by reformulating language-guided
grounding as open-vocabulary detection, which efficiently scales up model
prompting to thousands of category vocabularies and region descriptions while
maintaining the effectiveness of cross-modality fusion. To bridge the
granularity gap of different pixel-level tasks, APE equalizes semantic and
panoptic segmentation to proxy instance learning by considering any isolated
regions as individual instances. APE aligns vision and language representation
on broad data with natural and challenging characteristics all at once without
task-specific fine-tuning. The extensive experiments on over 160 datasets
demonstrate that, with only one-suit of weights, APE outperforms (or is on par
with) the state-of-the-art models, proving that an effective yet universal
perception for anything aligning and prompting is indeed feasible. Codes and
trained models are released at https://github.com/shenyunhang/APE.
