US 12,260,330 B2
Learning apparatus, learning method, inference apparatus, inference method, and recording medium
Azusa Sawada, Tokyo (JP); Soma Shiraishi, Tokyo (JP); and Takashi Shibata, Tokyo (JP)
Assigned to NEC CORPORATION, Tokyo (JP)
Appl. No. 17/640,926
Filed by NEC Corporation, Tokyo (JP)
PCT Filed Sep. 20, 2019, PCT No. PCT/JP2019/037007
§ 371(c)(1), (2) Date Mar. 7, 2022,
PCT Pub. No. WO2021/053815, PCT Pub. Date Mar. 25, 2021.
Prior Publication US 2022/0335291 A1, Oct. 20, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06V 10/771 (2022.01); G06N 3/08 (2023.01); G06V 10/77 (2022.01); G06V 10/82 (2022.01)
CPC G06N 3/08 (2013.01) [G06V 10/771 (2022.01); G06V 10/7715 (2022.01); G06V 10/82 (2022.01)] 8 Claims
OG exemplary drawing
 
1. A system comprising a learning apparatus and an inference apparatus for using outputs from the learning apparatus,
wherein the learning apparatus comprises:
a first memory storing first instructions; and
one or more first processors configured to execute the first instructions to:
learn, for each of a plurality of combinations of different attributes, a metric space including feature vectors which are extracted from sets of attribute-attached image data to which pieces of attribute information are respectively added, by using the sets of attribute-attached image data, such that a plurality of the metric spaces are learned for the plurality of combinations of different attributes; and
calculate, for each combination of different attributes, feature vectors from sets of case example image data and store the calculated feature vectors as case examples associated with the metric space in the first memory, and
wherein the inference apparatus comprises:
a second memory storing second instructions; and
one or more second processors configured to execute the second instructions to:
store, in the second memory and for each combination of different attributes, the feature vectors of the sets of case example image data as the case examples in association with the metric space learned for each combination of different attributes;
select one metric space by evaluating the plurality of metric spaces using a feature vector of selection image data;
recognize inference image data based on feature vectors extracted from the inference image data and the case examples associated with the one metric space; and
output a recognition result.