公开(公告)号：US20240273902A1

公开(公告)日：2024-08-15

申请号：US18439242

申请日：2024-02-12

Applicant: NEC Laboratories America, Inc.

Inventor： Deep Patel ,  Giovanni Milione ,  Kai Li ,  Farley Lai ,  Erik Kruus

IPC: G06V20/40 ,  G06V10/776 ,  G06V40/20 ,  G16H30/40

CPC classification number: G06V20/44 ,  G06V10/776 ,  G06V40/20 ,  G16H30/40

Abstract: Methods and systems of training a machine learning model include identifying an object or person related to an action in a first video. The object or person is copied from the first video to a second video to generate a third video. A machine learning model is trained using the first video and the third video.

公开(公告)号：US20230154167A1

公开(公告)日：2023-05-18

申请号：US17966017

申请日：2022-10-14

Applicant: NEC Laboratories America, Inc.

Inventor： Kai Li ,  Renqiang Min ,  Hans Peter Graf

IPC: G06V10/774 ,  G06V10/25 ,  G06V10/764

CPC classification number: G06V10/7747 ,  G06V10/25 ,  G06V10/765 ,  G06V2201/07

Abstract: A method for implementing source-free domain adaptive detection is presented. The method includes, in a pretraining phase, applying strong data augmentation to labeled source images to produce perturbed labeled source images and training an object detection model by using the perturbed labeled source images to generate a source-only model. The method further includes, in an adaptation phase, training a self-trained mean teacher model by generating a weakly augmented image and multiple strongly augmented images from unlabeled target images, generating a plurality of region proposals from the weakly augmented image, selecting a region proposal from the plurality of region proposals as a pseudo ground truth, detecting, by the self-trained mean teacher model, object boxes and selecting pseudo ground truth boxes by employing a confidence constraint and a consistency constraint, and training a student model by using one of the multiple strongly augmented images jointly with an object detection loss.

公开(公告)号：US20230153606A1

公开(公告)日：2023-05-18

申请号：US17968923

申请日：2022-10-19

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min ,  Kai Li ,  Hans Peter Graf ,  Zhiheng Li

IPC: G06N3/08 ,  G06N3/04 ,  G06F40/30 ,  G06V10/774 ,  G06V10/82 ,  G06F40/279

CPC classification number: G06N3/08 ,  G06N3/0454 ,  G06F40/30 ,  G06V10/774 ,  G06V10/82 ,  G06F40/279

Abstract: A method is provided that includes training a CLIP model to learn embeddings of images and text from matched image-text pairs. The text represents image attributes. The method trains a StyleGAN on images in a training dataset of matched image-text pairs. The method also trains, using a CLIP model guided contrastive loss which attracts matched text embedding pairs and repels unmatched pairs, a text-to-direction model to predict a text direction that is semantically aligned with an input text responsive to the input text and a random latent code. A triplet loss is used to learn text directions using the embeddings learned by the trained CLIP model. The method generates, by the trained StyleGAN, positive and negative synthesized images by respectively adding and subtracting the text direction in the latent space of the trained StyleGAN corresponding to a word for each of the words in the training dataset.

公开(公告)号：US11087184B2

公开(公告)日：2021-08-10

申请号：US16580199

申请日：2019-09-24

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min ,  Kai Li ,  Bing Bai ,  Hans Peter Graf

IPC: G06K9/62 ,  G06N3/08 ,  G06K9/46

Abstract: A computer-implemented method and system are provided for training a model for New Class Categorization (NCC) of a test image. The method includes decoupling, by a hardware processor, a feature extraction part from a classifier part of a deep classification model by reparametrizing learnable weight variables of the classifier part as a combination of learnable variables of the feature extraction part and of a classification weight generator of the classifier part. The method further includes training, by the hardware processor, the deep classification model to obtain a trained deep classification model by (i) learning the feature extraction part as a multiclass classification task, and (ii) episodically training the classifier part by learning a classification weight generator which outputs classification weights given a training image.