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 this https URL

通过自我监督的提示学习策略，使用视觉 - 语言模型以及逐步学习来扩展一组不同的、非重叠的超球形提示，以提高召回率和在下游任务中的性能。

DiPEx：无关类物体检测的提示扩展分散

DiPEx: Dispersing Prompt Expansion for Class-Agnostic Object Detection

Object detection is critical in autonomous driving, and it is more practical
yet challenging to localize objects of unknown categories: an endeavour known
as Class-Agnostic Object Detection (CAOD). Existing studies on CAOD
predominantly rely on ordinary cameras, but these frame-based sensors usually
have high latency and limited dynamic range, leading to safety risks in
real-world scenarios. In this study, we turn to a new modality enabled by the
so-called event camera, featured by its sub-millisecond latency and high
dynamic range, for robust CAOD. We propose Detecting Every Object in Events
(DEOE), an approach tailored for achieving high-speed, class-agnostic
open-world object detection in event-based vision. Built upon the fast
event-based backbone: recurrent vision transformer, we jointly consider the
spatial and temporal consistencies to identify potential objects. The
discovered potential objects are assimilated as soft positive samples to avoid
being suppressed as background. Moreover, we introduce a disentangled
objectness head to separate the foreground-background classification and novel
object discovery tasks, enhancing the model's generalization in localizing
novel objects while maintaining a strong ability to filter out the background.
Extensive experiments confirm the superiority of our proposed DEOE in
comparison with three strong baseline methods that integrate the
state-of-the-art event-based object detector with advancements in RGB-based
CAOD. Our code is available at this https URL

针对类别未知的对象进行高速、类别无关的开放世界目标检测，我们提出了使用事件相机的 DEOE 方法，该方法在时空一致性的基础上，结合了快速事件感知的骨干网络和解耦的物体性质分类头，实现了对新对象的定位以及背景过滤的强大能力。实验证实了我们提出的 DEOE 方法相对于现有的 RGB-based CAOD 对象检测方法的卓越性能。