Attentive Contexts for Object Detection

Understand

Modern deep neural network based object detection methods typically classify candidate proposals using their interior features.

• However, global and local surrounding contexts that are believed to be valuable for object detection are not fully exploited by existing methods yet.
• In this work, we take a step towards understanding what is a robust practice to extract and utilize contextual information to facilitate object detection in practice.
• Specifically, we consider the following two questions: "how to identify useful global contextual information for detecting a certain object?" and "how to exploit local context surrounding a proposal for better inferring its contents?".