US 12,136,255 B2
Dynamic, contextualized AI models
Yi Yang, Princeton, NJ (US); Murugan Sankaradas, Dayton, NJ (US); and Srimat Chakradhar, Manalapan, NJ (US)
Assigned to NEC Corporation, Tokyo (JP)
Filed by NEC Laboratories America, Inc., Princeton, NJ (US)
Filed on Jan. 18, 2022, as Appl. No. 17/577,664.
Claims priority of provisional application 63/139,669, filed on Jan. 20, 2021.
Prior Publication US 2022/0230421 A1, Jul. 21, 2022
Int. Cl. G06K 9/00 (2022.01); G06V 10/774 (2022.01); H04L 67/00 (2022.01); H04L 67/01 (2022.01)
CPC G06V 10/7747 (2022.01) [H04L 67/01 (2022.05); H04L 67/34 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A method for employing a semi-supervised learning approach to improve accuracy of a small model on an edge device, the method comprising:
collecting a plurality of frames from a plurality of video streams generated from a plurality of cameras, each camera associated with a respective small model, each small model deployed in the edge device;
sampling the plurality of frames to define sampled frames, including applying a mean Average Precision (mAP) policy that calculates a mAP for each frame based on a detection result from the small model and a ground truth using a detection result from the big model;
performing inference to the sampled frames by using a big model, the big model shared by all of the plurality of cameras and deployed in a cloud or cloud edge;
using the big model to generate labels for each of the sampled frames to generate training data; and
training each of the small models with the training data to generate updated small models on the edge device using a frame with a lowest mAP in a range.