MACHINE PERCEPTION AND ROBOTICS GROUP

Dept. of Robotics Science and Technology,
Chubu University

Attention branch network (ABN) can achieve high accuracy by visualizing the attention area of the network during inference and utilizing it in the recognition process. However, if the attention area does not highlight the target object to be recognized, it may cause recognition failure. While there is a method for fine-tuning the ABN using attention maps modified by human knowledge, it requires a lot of human labor and time because the attention map needs to be modified manually. The method introducing the attention mining branch (AMB) to ABN improves the attention area without using human knowledge by learning while considering whether the attention area is effective for recognition. However, even with AMB, attention regions other than the target object, i.e., unnecessary attention regions, may remain. In this paper, we investigate the effects of unwanted attention areas and propose a method to further improve the attention areas of ABN and AMB. In the evaluation experiments, we show that the proposed method improves the recognition accuracy and obtains an attention map with more gazed objects. Our evaluation experiments show that the proposed method improves the recognition accuracy and obtains an attention map that appropriately focuses on the target object to be recognized.