US 12,412,370 B2
Method and system of image hashing object detection for image processing
Yuqing Hou, Beijing (CN); Xiaolong Liu, Beijing (CN); Anbang Yao, Beijing (CN); and Yurong Chen, Beijing (CN)
Assigned to Intel Corporation, Santa Clara, CA (US)
Appl. No. 18/030,024
Filed by Intel Corporation, Santa Clara, CA (US)
PCT Filed Nov. 13, 2020, PCT No. PCT/CN2020/128632
§ 371(c)(1), (2) Date Apr. 3, 2023,
PCT Pub. No. WO2022/099600, PCT Pub. Date May 19, 2022.
Prior Publication US 2023/0368493 A1, Nov. 16, 2023
Int. Cl. G06V 10/764 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01)
CPC G06V 10/764 (2022.01) [G06V 10/776 (2022.01); G06V 10/82 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
obtaining image head class input data and image tail class input data differentiated from the head class input data and respectively of two images each of an object to be classified;
respectively inputting the head and tail class input data into two separate parallel representation neural networks being trained to respectively generate head and tail features, wherein the representation neural networks share at least some representation weights used to form the head and tail features;
inputting the head and tail features into at least one classifier neural network to generate class-related data;
generating a class-balanced loss of at least one of the classes of the class-related data comprising factoring an effective number of samples of individual classes; and
rebalancing an output sample distribution among the classes at the representation neural networks, classifier neural networks, or both by using the class-balanced loss.