Object detection is one of the hottest research directions in computer vision, has already made impressive progress in academia, and has many valuable applications in the industry. However, the mainstream detection methods still have two shortcomings: (1) even a model that is well trained using large amounts of data still cannot generally be used across different kinds of scenes; (2) once a model is deployed, it cannot autonomously evolve along with the accumulated unlabeled scene data. To address these problems, and inspired by visual knowledge theory, we propose a novel scene-adaptive evolution unsupervised video object detection algorithm that can decrease the impact of scene changes through the concept of object groups. We first extract a large number of object proposals from unlabeled data through a pre-trained detection model. Second, we build the visual knowledge dictionary of object concepts by clustering the proposals, in which each cluster center represents an object prototype. Third, we look into the relations between different clusters and the object information of different groups, and propose a graph-based group information propagation strategy to determine the category of an object concept, which can effectively distinguish positive and negative proposals. With these pseudo labels, we can easily fine-tune the pre-trained model. The effectiveness of the proposed method is verified by performing different experiments, and the significant improvements are achieved.

目标检测是计算机视觉领域最热门的研究方向之一，在学术界已经取得了令人瞩目的进展，在工业界也有许多有价值的应用。但是，主流的检测方法仍然存在两个不足：（1）即使是使用大量数据训练好的模型，仍然不能普遍用于不同类型的场景；（2）一旦模型被部署，它就不能随着积累的未标记场景数据自主进化。为了解决这些问题，并受视觉知识理论的启发，我们提出了一种新颖的场景自适应进化无监督视频对象检测算法，该算法可以通过对象组的概念来减少场景变化的影响。我们首先通过预训练的检测模型从未标记的数据中提取大量的目标提议。第二，我们通过对建议进行聚类来构建对象概念的视觉知识词典，其中每个聚类中心代表一个对象原型。第三，我们研究不同簇之间的关系以及不同组的对象信息，并提出基于图的组信息传播策略来确定对象概念的类别，可以有效区分正负提议。使用这些伪标签，我们可以轻松地微调预训练的模型。通过不同的实验验证了所提出方法的有效性，并取得了显着的改进。我们研究了不同簇之间的关系以及不同组的对象信息，并提出了一种基于图的组信息传播策略来确定对象概念的类别，可以有效区分正负提议。使用这些伪标签，我们可以轻松地微调预训练的模型。通过不同的实验验证了所提出方法的有效性，并取得了显着的改进。我们研究了不同簇之间的关系以及不同组的对象信息，并提出了一种基于图的组信息传播策略来确定对象概念的类别，可以有效区分正负提议。使用这些伪标签，我们可以轻松地微调预训练的模型。通过不同的实验验证了所提出方法的有效性，并取得了显着的改进。