  Class-agnostic object detection (OD) can be a cornerstone or a bottleneck for
many downstream vision tasks. Despite considerable advancements in bottom-up
and multi-object discovery methods that leverage basic visual cues to identify
salient objects, consistently achieving a high recall rate remains difficult
due to the diversity of object types and their contextual complexity. In this
work, we investigate using vision-language models (VLMs) to enhance object
detection via a self-supervised prompt learning strategy. Our initial findings
indicate that manually crafted text queries often result in undetected objects,
primarily because detection confidence diminishes when the query words exhibit
semantic overlap. To address this, we propose a Dispersing Prompt Expansion
(DiPEx) approach. DiPEx progressively learns to expand a set of distinct,
non-overlapping hyperspherical prompts to enhance recall rates, thereby
improving performance in downstream tasks such as out-of-distribution OD.
Specifically, DiPEx initiates the process by self-training generic parent
prompts and selecting the one with the highest semantic uncertainty for further
expansion. The resulting child prompts are expected to inherit semantics from
their parent prompts while capturing more fine-grained semantics. We apply
dispersion losses to ensure high inter-class discrepancy among child prompts
while preserving semantic consistency between parent-child prompt pairs. To
prevent excessive growth of the prompt sets, we utilize the maximum angular
coverage (MAC) of the semantic space as a criterion for early termination. We
demonstrate the effectiveness of DiPEx through extensive class-agnostic OD and
OOD-OD experiments on MS-COCO and LVIS, surpassing other prompting methods by
up to 20.1% in AR and achieving a 21.3% AP improvement over SAM. The code is
available at https://github.com/jason-lim26/DiPEx.
