Detect known and unknown object by first localize and then identify with CAT

Detect known and unknown object by first localize and then identify with CAT

CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection

arXiv paper abstract https://arxiv.org/abs/2301.01970

arXiv PDF paper https://arxiv.org/pdf/2301.01970.pdf

GitHub https://github.com/xiaomabufei/CAT

Open-world object detection (OWOD) ... requires the model trained from data on known objects to detect both known and unknown objects and incrementally learn to identify these unknown objects.

The existing works which employ standard detection framework and fixed pseudo-labelling mechanism (PLM) have ... problems

... observe ... humans subconsciously prefer to focus on all foreground objects and then identify each one in detail, rather than localize and identify a single object simultaneously, for alleviating the confusion.

... propose a novel solution called CAT: LoCalization and IdentificAtion Cascade Detection Transformer which decouples the detection process via the shared decoder in the cascade decoding way.

... propose the self-adaptive pseudo-labelling mechanism which combines the model-driven with input-driven PLM and self-adaptively generates robust pseudo-labels for unknown objects, significantly improving the ability of CAT to retrieve unknown objects.

... model outperforms the state-of-the-art in terms of all metrics in the task of OWOD, incremental object detection (IOD) and open-set detection.

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