Patent ID: 11954898
Assignee: SUPERB AI CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 1:
2. The learning method of claim 1, wherein, at the step of (c), the learning device instructs the second FC layer to apply the second FC operation to the second FC outputs, thereby outputting, as the pieces of the second class prediction information and the pieces of the second regression prediction information, (i) pieces of (2_1)-st class prediction information and pieces of (2_1)-st regression prediction information corresponding to the first objects and (ii) pieces of (2_2)-nd class prediction information and pieces of (2_2)-nd regression prediction information corresponding to the second objects, wherein the first objects and the second objects are selected among objects on the second training images, and,
wherein, at the step of (d), the learning device (i) acquires (2_1)-st class losses and (2_1)-st regression losses by referring to the pieces of the (2_1)-st class prediction information, the pieces of the (2_1)-st regression prediction information and their corresponding pieces of (2_1)-st class GT information and their corresponding pieces of (2_1)-st regression GT information, and acquires (2_2)-nd class losses and (2_2)-nd regression losses by referring to the pieces of the (2_2)-nd class prediction information, the pieces of the (2_2)-nd regression prediction information and their corresponding pieces of (2_2)-nd class GT information and pieces of (2_2)-nd regression GT information corresponding thereto, and (ii) (ii-1) backpropagating the (2_1)-st class losses and the (2_1)-st regression losses, thereby further training the third parameters of the first FC layer that have been trained in advance and the fourth parameters of the second FC layer that have not been trained; and (ii-2) backpropagating the (2_2)-nd class losses and the (2_2)-nd regression losses, thereby further training the fifth parameters of the second ROI proposal network that have not been trained, the third parameters of the first FC layer that have been trained in advance and the fourth parameters of the second FC layer that have not been trained.