公开(公告)号：US11669745B2

公开(公告)日：2023-06-06

申请号：US17080276

申请日：2020-10-26

Applicant: salesforce.com, inc.

Inventor： Chetan Ramaiah ,  Peng Tang ,  Caiming Xiong

IPC: G06F18/21 ,  G06N3/082 ,  G06F18/214

CPC classification number: G06F18/2178 ,  G06F18/2155 ,  G06N3/082

Abstract: A method for generating a neural network for detecting one or more objects in images includes generating one or more self-supervised proposal learning losses based on the one or more proposal features and corresponding proposal feature predictions. One or more consistency-based proposal learning losses are generated based on noisy proposal feature predictions and the corresponding proposal predictions without noise. A combined loss is generated using the one or more self-supervised proposal learning losses and one or more consistency-based proposal learning losses. The neural network is updated based on the combined loss.

公开(公告)号：US20210216828A1

公开(公告)日：2021-07-15

申请号：US17080276

申请日：2020-10-26

Applicant: salesforce.com, inc.

Inventor： Chetan Ramaiah ,  Peng Tang ,  Caiming Xiong

IPC: G06K9/62 ,  G06N3/08

Abstract: A method for generating a neural network for detecting one or more objects in images includes generating one or more self-supervised proposal learning losses based on the one or more proposal features and corresponding proposal feature predictions. One or more consistency-based proposal learning losses are generated based on noisy proposal feature predictions and the corresponding proposal predictions without noise. A combined loss is generated using the one or more self-supervised proposal learning losses and one or more consistency-based proposal learning losses. The neural network is updated based on the combined loss.

公开(公告)号：US20220237403A1

公开(公告)日：2022-07-28

申请号：US17161378

申请日：2021-01-28

Applicant: salesforce.com, inc.

Inventor： Pan Zhou ,  Peng Tang ,  Ran Xu ,  Chu Hong Hoi

IPC: G06K9/32 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04

Abstract: A system uses a neural network based model to perform scene text recognition. The system achieves high accuracy of prediction of text from scenes based on a neural network architecture that uses double attention mechanism. The neural network based model includes a convolutional neural network component that outputs a set of visual features and an attention extractor neural network component that determines attention scores based on the visual features. The visual features and the attention scores are combined to generate mixed features that are provided as input to a character recognizer component that determines a second attention score and recognizes the characters based on the second attention score. The system trains the neural network based model by adjusting the neural network parameters to minimize a multi-class gradient harmonizing mechanism (GHM) loss. The multi-class GHM loss varies based on a level of difficulty of the sample.