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Open-world object detection (OWOD), as a more general and challenging goal,requires the model trained from data on known objects to detect both known andunknown objects and incrementally learn to identify these unknown objects. Theexisting works which employ standard detection framework and fixedpseudo-labelling mechanism (PLM) have the following problems: (i) The inclusionof detecting unknown objects substantially reduces the model's ability todetect known ones. (ii) The PLM does not adequately utilize the prioriknowledge of inputs. (iii) The fixed selection manner of PLM cannot guaranteethat the model is trained in the right direction. We observe that humanssubconsciously prefer to focus on all foreground objects and then identify eachone in detail, rather than localize and identify a single objectsimultaneously, for alleviating the confusion. This motivates us to propose anovel solution called CAT: LoCalization and IdentificAtion Cascade DetectionTransformer which decouples the detection process via the shared decoder in thecascade decoding way. In the meanwhile, we propose the self-adaptivepseudo-labelling mechanism which combines the model-driven with input-drivenPLM and self-adaptively generates robust pseudo-labels for unknown objects,significantly improving the ability of CAT to retrieve unknown objects.Comprehensive experiments on two benchmark datasets, i.e., MS-COCO and PASCALVOC, show that our model outperforms the state-of-the-art in terms of allmetrics in the task of OWOD, incremental object detection (IOD) and open-setdetection.

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