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    SYSTEMS AND METHODS FOR ONLINE ADAPTATION FOR CROSS-DOMAIN STREAMING DATA
    2.
    发明公开
    SYSTEMS AND METHODS FOR ONLINE ADAPTATION FOR CROSS-DOMAIN STREAMING DATA 审中-公开

    公开(公告)号：US20230153307A1

    公开(公告)日：2023-05-18

    申请号：US17588022

    申请日：2022-01-28

    Applicant: salesforce.com, inc.

    Inventor： Luyu Yang ,  Mingfei Gao ,  Zeyuan Chen ,  Ran Xu ,  Chetan Ramaiah

    IPC: G06F16/2455 ,  G06F16/242 ,  G06N20/00

    CPC classification number: G06F16/24568 ,  G06F16/2425 ,  G06N20/00

    Abstract: Embodiments described herein provide an online domain adaptation framework based on cross-domain bootstrapping for online domain adaptation, in which the target domain streaming data is deleted immediately after adapted. At each online query, the data diversity is increased across domains by bootstrapping the source domain to form diverse combinations with the current target query. To fully take advantage of the valuable discrepancies among the diverse combinations, a set of independent learners are trained to preserve the differences. The knowledge of the learners is then integrated by exchanging their predicted pseudo-labels on the current target query to co-supervise the learning on the target domain, but without sharing the weights to maintain the learners' divergence.

    SYSTEMS AND METHODS FOR FIELD EXTRACTION FROM UNLABELED DATA
    3.
    发明申请
    SYSTEMS AND METHODS FOR FIELD EXTRACTION FROM UNLABELED DATA 有权

    公开(公告)号：US20220374631A1

    公开(公告)日：2022-11-24

    申请号：US17484618

    申请日：2021-09-24

    Applicant: salesforce.com, inc.

    Inventor： Mingfei Gao ,  Zeyuan Chen ,  Ran Xu

    IPC: G06K9/00 ,  G06N3/08

    Abstract: Embodiments described a field extraction system that does not require field-level annotations for training. Specifically, the training process is bootstrapped by mining pseudo-labels from unlabeled forms using simple rules. Then, a transformer-based structure is used to model interactions between text tokens in the input form and predict a field tag for each token accordingly. The pseudo-labels are used to supervise the transformer training. As the pseudo-labels are noisy, a refinement module that contains a sequence of branches is used to refine the pseudo-labels. Each of the refinement branches conducts field tagging and generates refined labels. At each stage, a branch is optimized by the labels ensembled from all previous branches to reduce label noise.

    PROCESSING FORMS USING ARTIFICIAL INTELLIGENCE MODELS
    4.
    发明申请
    PROCESSING FORMS USING ARTIFICIAL INTELLIGENCE MODELS 有权

    公开(公告)号：US20230133690A1

    公开(公告)日：2023-05-04

    申请号：US17453070

    申请日：2021-11-01

    Applicant: Salesforce.com, inc.

    Inventor： Mingfei Gao ,  Ran Xu

    IPC: G06K9/00 ,  G06F40/174 ,  G06F40/205 ,  G06N20/00

    Abstract: An application server may receive an input document including a set of input text fields and an input key phrase querying a value for a key-value pair that corresponds to one or more of the set of input text fields. The application server may extract, using an optical character recognition model, a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document. After extraction, the application server may input the extracted set of character strings and the set of two-dimensional locations into a machine learned model that is trained to compute a probability that a character string corresponds to the value for the key-value pair. The application server may then identify the value for the key-value pair corresponding to the input key phrase and may out the identified value.

    Two-stage online detection of action start in untrimmed videos
    5.
    发明授权
    Two-stage online detection of action start in untrimmed videos 有权

    公开(公告)号：US10902289B2

    公开(公告)日：2021-01-26

    申请号：US16394992

    申请日：2019-04-25

    Applicant: salesforce.com, inc.

    Inventor： Mingfei Gao ,  Richard Socher ,  Caiming Xiong

    IPC: G06K9/62 ,  G06K9/00

    Abstract: Embodiments described herein provide a two-stage online detection of action start system including a classification module and a localization module. The classification module generates a set of action scores corresponding to a first video frame from the video, based on the first video frame and video frames before the first video frames in the video. Each action score indicating a respective probability that the first video frame contains a respective action class. The localization module is coupled to the classification module for receiving the set of action scores from the classification module and generating an action-agnostic start probability that the first video frame contains an action start. A fusion component is coupled to the localization module and the localization module for generating, based on the set of action scores and the action-agnostic start probability, a set of action-specific start probabilities, each action-specific start probability corresponding to a start of an action belonging to the respective action class.

    Two-Stage Online Detection of Action Start In Untrimmed Videos
    6.
    发明申请
    Two-Stage Online Detection of Action Start In Untrimmed Videos 审中-公开

    公开(公告)号：US20200302236A1

    公开(公告)日：2020-09-24

    申请号：US16394992

    申请日：2019-04-25

    Applicant: Salesforce.com, Inc,

    Inventor： Mingfei Gao ,  Richard Socher ,  Caiming Xiong

    IPC: G06K9/62 ,  G06K9/00

    Abstract: Embodiments described herein provide a two-stage online detection of action start system including a classification module and a localization module. The classification module generates a set of action scores corresponding to a first video frame from the video, based on the first video frame and video frames before the first video frames in the video. Each action score indicating a respective probability that the first video frame contains a respective action class. The localization module is coupled to the classification module for receiving the set of action scores from the classification module and generating an action-agnostic start probability that the first video frame contains an action start. A fusion component is coupled to the localization module and the localization module for generating, based on the set of action scores and the action-agnostic start probability, a set of action-specific start probabilities, each action-specific start probability corresponding to a start of an action belonging to the respective action class.

    IMAGE ANALYSIS BASED DOCUMENT PROCESSING FOR INFERENCE OF KEY-VALUE PAIRS IN NON-FIXED DIGITAL DOCUMENTS
    8.
    发明申请
    IMAGE ANALYSIS BASED DOCUMENT PROCESSING FOR INFERENCE OF KEY-VALUE PAIRS IN NON-FIXED DIGITAL DOCUMENTS 有权

    公开(公告)号：US20220215195A1

    公开(公告)日：2022-07-07

    申请号：US17140987

    申请日：2021-01-04

    Applicant: salesforce.com, inc.

    Inventor： Mingfei Gao ,  Zeyuan Chen ,  Le Xue ,  Ran Xu ,  Caiming Xiong

    IPC: G06K9/00 ,  G06F40/289 ,  G06F40/186

    Abstract: An online system extracts information from non-fixed form documents. The online system receives an image of a form document and obtains a set of phrases and locations of the set of phrases on the form image. For at least one field, the online system determines key scores for the set of phrases. The online system identifies a set of candidate values for the field from the set of identified phrases and identifies a set of neighbors for each candidate value from the set of identified phrases. The online system determines neighbor scores, where a neighbor score for a candidate value and a respective neighbor is determined based on the key score for the neighbor and a spatial relationship of the neighbor to the candidate value. The online system selects a candidate value and a respective neighbor based on the neighbor score as the value and key for the field.

    Two-Stage Online Detection of Action Start In Untrimmed Videos
    9.
    发明申请
    Two-Stage Online Detection of Action Start In Untrimmed Videos 审中-公开

    公开(公告)号：US20200302178A1

    公开(公告)日：2020-09-24

    申请号：US16394964

    申请日：2019-04-25

    Applicant: salesforce.com, inc.

    Inventor： Mingfei Gao ,  Richard Socher ,  Caiming Xiong

    IPC: G06K9/00 ,  G06K9/62 ,  G06N3/04

    Abstract: Embodiments described herein provide a two-stage online detection of action start system including a classification module and a localization module. The classification module generates a set of action scores corresponding to a first video frame from the video, based on the first video frame and video frames before the first video frames in the video. Each action score indicating a respective probability that the first video frame contains a respective action class. The localization module is coupled to the classification module for receiving the set of action scores from the classification module and generating an action-agnostic start probability that the first video frame contains an action start. A fusion component is coupled to the localization module and the localization module for generating, based on the set of action scores and the action-agnostic start probability, a set of action-specific start probabilities, each action-specific start probability corresponding to a start of an action belonging to the respective action class.

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