US 12,067,082 B2
Temporal contrastive learning for semi-supervised video action recognition
Rameswar Panda, Medford, MA (US); Rogerio Schmidt Feris, West Hartford, CT (US); and Abir Das, West Bengal (IN)
Assigned to International Business Machines Corporation, Armonk, NY (US); and Indian Institute of Technology, Kharagpur West Bengal (IN)
Filed by International Business Machines Corporation, Armonk, NY (US); and Indian Institute of Technology Kharagpur, West Bengal (IN)
Filed on Oct. 29, 2021, as Appl. No. 17/515,380.
Prior Publication US 2023/0138254 A1, May 4, 2023
Int. Cl. G06F 18/214 (2023.01); G06F 16/783 (2019.01); G06F 18/22 (2023.01); G06F 18/24 (2023.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06V 20/40 (2022.01)
CPC G06F 18/2155 (2023.01) [G06F 16/783 (2019.01); G06F 18/22 (2023.01); G06F 18/24 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06V 20/41 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
training a base pathway of a computerized two-pathway video action recognition model using a plurality of labeled video samples;
training the base pathway of the computerized two-pathway video action recognition model using a plurality of unlabeled video samples at a first framerate;
training an auxiliary pathway of the computerized two-pathway video action recognition model using a plurality of the unlabeled video samples at a second framerate, the second framerate being slower than the first framerate;
wherein said training of said base pathway using said plurality of labeled video samples, said training of said base pathway using said plurality of unlabeled video samples at said first framerate, and said training of said auxiliary pathway using said plurality of unlabeled video samples at said second framerate, result in a trained computerized two-pathway video action recognition model;
categorizing a candidate video using the trained computerized two-pathway video action recognition model; and
storing the categorized candidate video in a computer-accessible video database system for information retrieval.