公开(公告)号：US20230124495A1

公开(公告)日：2023-04-20

申请号：US18050757

申请日：2022-10-28

Applicant: Intel Corporation

Inventor： Sovan Biswas ,  Anthony Daniel Rhodes ,  Ramesh Radhakrishna Manuvinakurike ,  Giuseppe Raffa ,  Richard Beckwith

IPC: G06V20/40 ,  G06V10/764 ,  G06V10/82

Abstract: Disclosed is a technical solution to process a video that captures actions to be performed for completing a task based on a chronological sequence of stages within the task. An example system may identify an action sequence from an instruction for the task. The system inputs the action sequence into a trained model (e.g., a recurrent neural network), which outputs the chronological sequence of stages. The RNN may be trained through self-supervised learning. The system may input the video and the chronological sequence of stages into another trained model, e.g., a temporal convolutional network. The other trained model may include hidden layers arranged before an attention layer. The hidden layers may extract features from the video and feed the features into the attention layer. The attention layer may determine attention weights of the features based on the chronological sequence of stages.

公开(公告)号：US20230024803A1

公开(公告)日：2023-01-26

申请号：US17936941

申请日：2022-09-30

Applicant: Intel Corporation

Inventor： Sovan Biswas ,  Anthony Rhodes ,  Ramesh Manuvinakurike ,  Giuseppe Raffa ,  Richard Beckwith

IPC: G06V20/40 ,  G06V20/70 ,  G06V10/776 ,  G06V10/774 ,  G06V10/94

Abstract: Systems, apparatuses, and methods include technology that generates final frame predictions for a first plurality of frames of a video, where the first plurality of frames is associated with unlabeled data. The technology predicts an ordered list of actions for the first plurality of frames based on the final frame predictions, and temporally aligning the ordered list of actions to the final frame predictions to generate labels.

公开(公告)号：US20240104915A1

公开(公告)日：2024-03-28

申请号：US18459824

申请日：2023-09-01

Applicant: Intel Corporation

Inventor： Anthony Daniel Rhodes ,  Byungsu Min ,  Subarna Tripathi ,  Giuseppe Raffa ,  Sovan Biswas

IPC: G06V10/82 ,  G06V10/75 ,  G06V10/86 ,  G06V20/40

CPC classification number: G06V10/82 ,  G06V10/751 ,  G06V10/86 ,  G06V20/46 ,  G06V20/49

Abstract: Machine learning models can process a video and generate outputs such as action segmentation assigning portions of the video to a particular action, or action classification assigning an action class for each frame of the video. Some machine learning models can accurately make predictions for short videos but may not be particularly suited for performing action segmentation for long duration, structured videos. An effective machine learning model may include a hybrid architecture involving a temporal convolutional network and a bi-directional graph neural network. The machine learning model can process long duration structured videos by using a temporal convolutional network as a first pass action segmentation model to generate rich, frame-wise features. The frame-wise features can be converted into a graph having forward edges and backward edges. A graph neural network can process the graph to refine a final fine-grain per-frame action prediction.

公开(公告)号：US20220382787A1

公开(公告)日：2022-12-01

申请号：US17816468

申请日：2022-08-01

Applicant: Intel Corporation

Inventor： Anthony Rhodes ,  Sovan Biswas ,  Giuseppe Raffa

IPC: G06F16/28

Abstract: Systems, apparatuses, and methods include technology that extracts a plurality of features from the input data. The technology generates a confidence metric for the plurality of features. The confidence metric corresponds to a degree that at least one feature of the plurality of features is relevant for classification of the input data. The technology categorizes the input data into a category based on the plurality of features and the confidence metric

公开(公告)号：US20250117633A1

公开(公告)日：2025-04-10

申请号：US18987302

申请日：2024-12-19

Applicant: Intel Corporation

Inventor： Anthony Daniel Rhodes ,  Ramesh Radhakrishna Manuvinakurike ,  Sovan Biswas ,  Giuseppe Raffa ,  Lama Nachman

IPC: G06N3/0475

Abstract: Predictive uncertainty of a generative machine learning model may be estimated. The generative machine learning model may be a large language model or large multi-modal model. A datum may be input into the generative machine learning model. The generative machine learning model may generate outputs from the datum. Latent embeddings for the outputs may be extracted from the generative machine learning model. A covariance matrix with respect to the latent embeddings may be computed. The covariance matrix may be a two-dimensional matrix, such as a square matrix. The predictive uncertainty of the generative machine learning model may be estimated using the covariance matrix. For instance, the matrix entropy of the covariance matrix may be determined. The matrix entropy may be an approximated dimension of a latent semantic manifold spanned by the outputs of the generative machine learning model and may indicate the predictive uncertainty of the generative machine learning model.

公开(公告)号：US20230071760A1

公开(公告)日：2023-03-09

申请号：US18050929

申请日：2022-10-28

Applicant: Intel Corporation

Inventor： Anthony Daniel Rhodes ,  Sovan Biswas ,  Giuseppe Raffa

IPC: G06N3/08 ,  G06N3/04

Abstract: Disclosed is a technical solution to calibrate confidence scores of classification networks. A classification network has been trained to receive an input and output a label of the input that indicates a class of the input. The classification network also outputs a confidence score of the label, which indicates a likelihood of the input falling into the class, i.e., a confidence level of the classification network that the label is correct. To calibrate the confidence of the classification network, a logit transformation function may be added into the classification network. The logic transformation function may be an entropy-based function and have learnable parameters, which may be trained by inputting calibration samples into the classification network and optimizing a negative log likelihood based on the labels generated by the classification network and ground-truth labels of the calibration samples. The trained logic transformation function can be used to compute reliable confidence scores.

公开(公告)号：US20230010230A1

公开(公告)日：2023-01-12

申请号：US17932339

申请日：2022-09-15

Applicant: Intel Corporation

Inventor： Anthony Rhodes ,  Sovan Biswas ,  Giuseppe Raffa

IPC: G06V10/776 ,  G06V10/82 ,  G06V20/40 ,  G06V20/70 ,  G06V10/774

Abstract: Systems, apparatuses, and methods include technology that identifies, with a neural network, that a predetermined amount of a first action is completed at a first portion of a plurality of portions. A subset of the plurality of portions collectively represents the first action. The technology generates a first loss based on the predetermined amount of the first action being identified as being completed at the first portion. The technology updates the neural network based on the first loss.