Unsupervised object detection with scene-adaptive concept learning

Abstract

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.

摘要

目标检测是机器视觉领域最热门的研究方向之一, 在学术界已取得令人瞩目的成果, 在工业界也存在许多有价值的应用. 然而, 主流的检测方法仍有两个缺陷: (1) 即使是经过大量数据有效训练的模型, 仍然无法很好地泛化到新场景中; (2) 模型一旦部署到位, 则无法随着不断累积的无标注数据自主进化. 为克服上述问题, 受视觉知识理论启发, 提出一种场景自适应进化的无监督视频目标检测算法, 该算法可利用目标群体概念, 降低场景变化带来的不利影响. 首先通过预训练检测模型从无标注数据中提取大量候选目标, 然后对候选目标聚类, 构建目标概念的视觉知识字典, 其中各个聚类中心代表一种目标原型. 其次, 通过研究不同目标簇和不同群体目标信息之间的关系, 提出基于图的群体信息传播策略以判断目标概念的归属, 可有效区分候选目标. 最终, 利用收集到的伪类标微调预训练模型, 实现算法对新场景的自适应. 算法的有效性得到多个不同实验的验证, 且性能提升显著.