公开(公告)号：US20240104915A1

公开(公告)日：2024-03-28

申请号：US18459824

申请日：2023-09-01

申请人： Intel Corporation

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

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

CPC分类号： G06V10/82 ,  G06V10/751 ,  G06V10/86 ,  G06V20/46 ,  G06V20/49

摘要： 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：US20230071760A1

公开(公告)日：2023-03-09

申请号：US18050929

申请日：2022-10-28

申请人： Intel Corporation

发明人： Anthony Daniel Rhodes ,  Sovan Biswas ,  Giuseppe Raffa

IPC分类号： G06N3/08 ,  G06N3/04

摘要： 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：US20230124495A1

公开(公告)日：2023-04-20

申请号：US18050757

申请日：2022-10-28

申请人： Intel Corporation

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

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

摘要： 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：US20240144447A1

公开(公告)日：2024-05-02

申请号：US18532273

申请日：2023-12-07

申请人： Intel Corporation

发明人： Anthony Daniel Rhodes ,  Ilke Demir

IPC分类号： G06T5/70 ,  G06V10/30 ,  G06V10/32 ,  G06V10/46

CPC分类号： G06T5/70 ,  G06V10/30 ,  G06V10/32 ,  G06V10/462

摘要： Deep learning models, such as diffusion models, can synthesize images from noise. Diffusion models implement a complex denoising process involving many denoising operations. It can be a challenge to understand the mechanics of diffusion models. To better understand how and when structure is formed, saliency maps and concept formation intensity can be extracted from the sampling network of a diffusion model. Using the input map and the output map of a given denoising operation in a sampling network, a noise gradient map representative of the predicted noise of a given denoising operation can be determined. The noise gradient maps from the denoising operations at different indices can be combined to generate a saliency map. A concept formation intensity value can be determined from a noise gradient map. Concept formation intensity values from the denoising operations at different indices can be plotted.

公开(公告)号：US20220101138A1

公开(公告)日：2022-03-31

申请号：US17548692

申请日：2021-12-13

申请人： Intel Corporation

发明人： Anthony Daniel Rhodes ,  Julio Cesar Zamora Esquivel ,  Lama Nachman

IPC分类号： G06N3/08 ,  G06N3/04

摘要： An apparatus is provided for deep learning. The apparatus accesses a neural network including an input layer, hidden layers, and an output layer. The apparatus adds an activation function to one or more of the hidden layers of the hidden layers and output layer. The activation function includes a tunable parameter, the value of which can be adjusted during the training of the neural network. The apparatus trains the neural network by inputting training samples into the neural network and determining internal parameters of the neural network based on the training samples. Determining the internal parameters includes determining a value of the tunable parameter based on the training samples. The apparatus may determine two different values of the tunable parameter for two different layers. The activation function may include another tunable parameter. The apparatus can determine a value for the other tunable parameter during the training of the neural network.