  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 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 the following problems: (i) The inclusion
of detecting unknown objects substantially reduces the model's ability to
detect known ones. (ii) The PLM does not adequately utilize the priori
knowledge of inputs. (iii) The fixed selection manner of PLM cannot guarantee
that the model is trained in the right direction. We observe that 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. This motivates us to 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. In the meanwhile, we 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.
Comprehensive experiments on two benchmark datasets, i.e., MS-COCO and PASCAL
VOC, show that our 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.
