FOC OSOD: Focus on Classification One-Shot Object Detection

no code implementations • 1 Jan 2021 • Hanqing Yang, Huaijin Pi, SABA GHORBANI BARZEGAR, Yu Zhang

This paper analyzes the serious false positive problem in OSOD and proposes a Focus on Classification One-Shot Object Detection (FOC OSOD) framework, which is improved in two important aspects: (1) classification cascade head with the fixed IoU threshold can enhance the robustness of classification by comparing multiple close regions; (2) classification region deformation on the query feature and the reference feature to obtain a more effective comparison region.

Classification General Classification +3