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.
翻译:开放世界天体探测(OWOD)是一个更加普遍和具有挑战性的目标,它要求从已知天体的数据中培训模型,以探测已知和未知天体,并逐步学习识别这些未知天体。使用标准探测框架和固定假标签机制(PLM)的现有工程存在以下问题:(一) 纳入检测未知天体,大大降低了模型检测已知天体的能力。 (二) PLM没有充分利用输入的先验知识。 (三) PLM的固定选择方式无法保证模型得到正确方向的培训。我们发现,人类潜意识地倾向于侧重于所有前地天体,然后详细确定每个对象,而不是本地化和同时识别一个单一对象,以缓解混乱。这促使我们提出一个新的解决方案,即CAT:LoCaliz和IcentifificAcade探测变异体,它通过星级解码解码的共享模型解码,将检测过程分解。与此同时,我们建议采用自我调化的伪标签机制,将模型和CO-CLM 的测算功能与未知的硬质的硬体实验能力结合起来,将模型和硬体的硬体的硬体的硬体的硬体的硬体实验,将硬体的硬体的硬体的硬体的硬体的硬体的硬体的硬体的硬体的硬体的硬体的硬体的硬体试验的硬体试验的硬体的硬体试验, 显示的硬体的硬体的硬体的硬体试验的硬体试验的自我定位的自我定位的硬体 。