Detect unknown objects using unsupervised region proposal methods with MEPU
Detect unknown objects using unsupervised region proposal methods with MEPU
Unsupervised Recognition of Unknown Objects for Open-World Object Detection
arXiv paper abstract https://arxiv.org/abs/2308.16527
arXiv PDF paper https://arxiv.org/pdf/2308.16527.pdf
GitHub https://github.com/frh23333/mepu-owod
Open-World Object Detection (OWOD) … is … detecting both known and unknown objects and incrementally learning newly introduced knowledge.
Current … models … focus on pseudo-labeling regions with high objectness scores as unknowns, whose performance relies heavily on the supervision of known objects.
While they can detect the unknowns that exhibit similar features to the known objects, they suffer from a severe label bias problem that they tend to detect all regions (including unknown object regions) that are dissimilar to the known objects as part of the background.
… this paper proposes a novel approach that learns an unsupervised discriminative model to recognize true unknown objects from raw pseudo labels generated by unsupervised region proposal methods.
The resulting model can be further refined by a classification-free self-training method which iteratively extends pseudo unknown objects to the unlabeled regions.
… method … significantly outperforms the prior SOTA in detecting unknown objects while maintaining competitive performance of detecting known object classes …
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