Comparison Network for One-Shot Conditional Object Detection

Understand

The current advances in object detection depend on large-scale datasets to get good performance.

• However, there may not always be sufficient samples in many scenarios, which leads to the research on few-shot detection as well as its extreme variation one-shot detection.
• In this paper, the one-shot detection has been formulated as a conditional probability problem.
• With this insight, a novel one-shot conditional object detection (OSCD) framework, referred as Comparison Network (ComparisonNet), has been proposed.