公开(公告)号：US20250069356A1

公开(公告)日：2025-02-27

申请号：US18815217

申请日：2024-08-26

Applicant: SRI International

Inventor： Aswin NADAMUNI RAGHAVAN ,  Jun HU ,  David C. ZHANG ,  Michael R. LOMNITZ ,  Yuzheng ZHANG ,  Michael PIACENTINO ,  Philip MILLER ,  Zachary A. DANIELS ,  Saurabh FARKYA ,  Abrar A. RAHMAN ,  Abdelrahman SHARAFELDIN

IPC: G06V10/25 ,  G06V10/74

Abstract: A method, apparatus, and system for object detection on an edge device include projecting a hyperdimensional vector of a query request for an image received at the edge device into a hyperdimensional embedding space to identify at least one exemplar in the hyperdimensional embedding space having a predetermined measure of similarity to the query request using a network trained to: generate a respective hyperdimensional image vector and a respective hyperdimensional text vector for the image and received text descriptions of the image, generate a hyperdimensional query text vector of the query request, combine and embed respective ones of the hyperdimensional image vectors and the hyperdimensional text vectors into a hyperdimensional embedding space to generate respective exemplars, project the hyperdimensional query text vector into the hyperdimensional embedding space, and determine a similarity measure between the hyperdimensional query text vector and at least one of the respective exemplars.

公开(公告)号：US20250094810A1

公开(公告)日：2025-03-20

申请号：US18823282

申请日：2024-09-03

Applicant: SRI International

Inventor： Zachary A. DANIELS ,  Jun HU ,  Michael R. LOMNITZ ,  Philip MILLER ,  Aswin NADAMUNI RAGHAVAN ,  Yuzheng ZHANG ,  Michael PIACENTINO ,  David C. ZHANG ,  Michael ISNARDI ,  Saurabh FARKYA

IPC: G06N3/084

Abstract: Method and apparatus for processing input information using an adaptable and continually learning neural network architecture comprising an encoder, at least one adaptor and at least one reconfigurator. The encoder, at least one reconfigurator and at least one adaptor determine whether the input information is out-of-distribution or in-distribution. If the input information is in distribution, the architecture extracts features from the input information, creates hyperdimensional vectors representing the features and classifies the hyperdimensional vectors. If the input information is out of distribution, the architecture creates at least one adaptor to operate with the encoder and the at least one reconfigurator to extract features from the input information, create hyperdimensional vectors representing the features and classify the hyperdimensional vectors.