DualMem: Bypassing the Objectness Bottleneck for Calibrated Unknown-Stream Filtering in Open-World Object Detection
arXiv:2605.23634v1 Announce Type: cross Abstract: Open-world object detection (OWOD) requires detectors to localize known classes while identifying unknown objects for future incremental learning. We find that the unknown prediction streams of strong OWOD detectors are heavily polluted: on M-OWODB, across PROB, OW-DETR, and HypOW, future-task positive unknowns make up less than 10% of unknown predictions, whereas background false positives account for 46-71%. We show that this is not a missing-information problem, but an information bottleneck at the objectness head. On PROB Task 1, a linear p
This research addresses a critical limitation in open-world object detection, a foundational component for robust autonomous AI systems, reflecting increased focus on AI reliability.
Improved object detection for unknown objects directly enhances the capability of AI systems to operate in dynamic, real-world environments, reducing false positives and improving learning efficiency.
The identification and proposed bypass of the 'objectness bottleneck' enable more accurate and efficient identification of novel objects, moving towards more generalizable AI perception.
- · AI developers (especially in robotics and autonomous systems)
- · Computer vision research community
- · Companies deploying AI in unstructured environments
- · Systems relying on current, less efficient OWOD methods
- · Competitors with less robust unknown object identification
More reliable deployment of AI in complex, unsupervised settings for tasks like surveillance, logistics, and autonomous driving.
Accelerated development and adoption of AI agents that can adapt to unforeseen situations and continuously learn from novel sensory input.
Enhanced AI capabilities contribute to a broader shift towards agentic systems that can operate with minimal human oversight in diverse applications.
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Read at arXiv cs.AI