公开(公告)号：US20240404235A1

公开(公告)日：2024-12-05

申请号：US18694735

申请日：2022-09-22

Applicant: UCL BUSINESS LTD.

Inventor： Watjana LILAONITKUL ,  Adam DUBIS ,  James WILLOUGHBY

IPC: G06V10/25 ,  G06V10/26 ,  G06V10/30 ,  G06V10/77 ,  G06V10/774 ,  G06V10/82

Abstract: A computer-implemented method of enhancing object detection in a digital image of known underlying structure using pre-processed images with underlying structure and with any objects detected and bounding boxes inserted over the objects, the method comprising: extracting or generating images with the underlying structure but without objects detected as negative images; extracting images with the underlying structure and with an object detected as positive images; inputting pairs of negative and positive images through a feature extraction section in a neural network to extract feature vectors of the images; contrasting feature vectors of each pair of positive and negative images and thereby provide a contrast vector and gating the result to form an attention vector; processing the attention vector and the feature vector of the positive image to produce an output.

公开(公告)号：US20240395023A1

公开(公告)日：2024-11-28

申请号：US18694711

申请日：2022-09-22

Applicant: UCL BUSINESS LTD.

Inventor： Watjana LILAONITKUL ,  Adam DUBIS ,  Mustafa ARIKAN

IPC: G06V10/774 ,  G06V10/762 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G16H30/40

Abstract: A computer-implemented method of active learning for computer vision in digital images, comprising: inputting labelled image training examples into an artificial neural network in a training phase; training a computer vision model using the labelled training examples; carrying out a prediction task on each image of an unlabelled training set of unlabelled, unseen images using the model; calculating an uncertainty metric for the predictions in each image of the unlabelled training set; calculating a similarity metric for the unlabelled training set representing similarities between the images in the training set; selecting images from the unlabelled training set, in dependence upon both the similarity metric and the uncertainty metric of each image, to design a training set for labelling which tends to both lower the similarity between the selected images and increase the uncertainty of the selected images.