Various contextual information has been employed by many approaches for visual detection tasks. However, most of the existing approaches only focus on specific context for specific tasks. In this paper, GMC, a general framework is proposed for multistage context learning and utilization, with various deep network architectures for various visual detection tasks. The GMC framework encompasses three stages: preprocessing, training, and post-processing. In the preprocessing stage, the representation of local context is enhanced by utilizing commonly used labeling standards. During the training stage, semantic context information is fused with visual information, leveraging prior knowledge from the training dataset to capture semantic relationships. In the post-processing stage, general topological relations and semantic masks for stuff are incorporated to enable spatial context reasoning between objects. The proposed framework provides a comprehensive and adaptable solution for context learning and utilization in visual detection scenarios. The framework offers flexibility with user-defined configurations and provide adaptability to diverse network architectures and visual detection tasks, offering an automated and streamlined solution that minimizes user effort and inference time in context learning and reasoning. Experimental results on the visual detection tasks, for storefront object detection, pedestrian detection and COCO object detection, demonstrate that our framework outperforms previous state-of-the-art detectors and transformer architectures. The experiments also demonstrate that three contextual learning components can not only be applied individually and in combination, but can also be applied to various network architectures, and its flexibility and effectiveness in various detection scenarios.

提出了GMC框架，用于多阶段上下文学习和利用的通用框架，利用各种深度网络架构进行各种视觉检测任务。GMC框架包括三个阶段：预处理、训练和后处理，并通过结合语义上下文信息和视觉信息，以及利用训练数据集中的先验知识捕捉语义关系，在对象之间实现空间上下文推理。框架灵活适应用户定义的配置和多样化的网络架构和视觉检测任务，提供了一个自动化和高效的解决方案，在上下文学习和推理中最大程度地减少用户的工作量和推理时间，并在视觉检测任务中展示了超越先前状态-of-the-art检测器和变换器架构的实验结果。实验还证明了三个上下文学习组件不仅可以单独应用，也可以组合应用，并且可以应用于各种网络架构，在各种检测场景下具有灵活性和高效性。

GMC: 一种用于视觉检测任务的多阶段上下文学习与利用的通用框架