Detecting Unknown Objects via Energy-based Separation for Open World Object Detection
In this work, we tackle the problem of Open World Object Detection (OWOD). This challenging scenario requires the detector to incrementally learn to classify known objects without forgetting while identifying unknown objects without supervision. Previous OWOD methods have enhanced the unknown discovery process and employed memory replay to mitigate catastrophic forgetting. However, since existing methods heavily rely on the detector's known class predictions for detecting unknown objects, they struggle to effectively learn and recognize unknown object representations. Moreover, while memory replay mitigates forgetting of old classes, it often sacrifices the knowledge of newly learned clas...
| Authors | Jun-Woo Heo, Keonhee Park, Gyeong-Moon Park |
|---|---|
| Primary category | cs.CV |
| Published | 31 March 2026 |
| Last updated at source | 28 May 2026 |
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