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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.
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公开(公告)号：US11699297B2
公开(公告)日：2023-07-11
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: G06V30/413 , G06F40/186 , G06F40/289 , G06V30/412 , G06F40/295 , G06V30/10 , G06V10/40
CPC classification number: G06V30/413 , G06F40/186 , G06F40/289 , G06V30/412 , G06F40/295 , G06V10/40 , G06V30/10
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.
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公开(公告)号：US20220366317A1
公开(公告)日：2022-11-17
申请号：US17484623
申请日：2021-09-24
Applicant: salesforce.com, inc.
Inventor： Mingfei Gao , Zeyuan Chen , Ran Xu
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.
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公开(公告)号：US20220083819A1
公开(公告)日：2022-03-17
申请号：US17457163
申请日：2021-12-01
Applicant: salesforce.com, inc.
Inventor： Ankit Chadha , Caiming Xiong , Ran Xu
Abstract: Computing systems may support image classification and image detection services, and these services may utilize object detection/image classification machine learning models. The described techniques provide for normalization of confidence scores corresponding to manipulated target images and for non-max suppression within the range of confidence scores for manipulated images. In one example, the techniques provide for generating different scales of a test image, and the system performs normalization of confidence scores corresponding to each scaled image and non-max suppression per scaled image These techniques may be used to provide more accurate image detection (e.g., object detection and/or image classification) and may be used with models that are not trained on modified image sets. The model may be trained on a standard (e.g. non-manipulated) image set but used with manipulated target images and the described techniques to provide accurate object detection.
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公开(公告)号：US20210357687A1
公开(公告)日：2021-11-18
申请号：US16931228
申请日：2020-07-16
Applicant: salesforce.com, inc.
Inventor： Mingfei Gao , Yingbo Zhou , Ran Xu , Caiming Xiong
Abstract: Embodiments described herein provide systems and methods for a partially supervised training model for online action detection. Specifically, the online action detection framework may include two modules that are trained jointly—a Temporal Proposal Generator (TPG) and an Online Action Recognizer (OAR). In the training phase, OAR performs both online per-frame action recognition and start point detection. At the same time, TPG generates class-wise temporal action proposals serving as noisy supervisions for OAR. TPG is then optimized with the video-level annotations. In this way, the online action detection framework can be trained with video-category labels only without pre-annotated segment-level boundary labels.
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Patent Agency Ranking
公开(公告)号：US20230162490A1
公开(公告)日：2023-05-25
申请号：US17589725
申请日：2022-01-31
Applicant: salesforce.com, inc.
Inventor： Shu Zhang , Junnan Li , Ran Xu , Caiming Xiong , Chetan Ramaiah
IPC: G06V10/776 , G06V10/74 , G06F40/284 , G06F40/166 , G06F40/126 , G06V10/80 , G06F16/583 , G06F16/56
CPC classification number: G06V10/776 , G06V10/761 , G06F40/284 , G06F40/166 , G06F40/126 , G06V10/806 , G06F16/5846 , G06F16/56
Abstract: Embodiments described herein a CROss-Modal Distribution Alignment (CROMDA) model for vision-language pretraining, which can be used for retrieval downstream tasks. In the CROMDA mode, global cross-modal representations are aligned on each unimodality. Specifically, a uni-modal global similarity between an image/text and the image/text feature queue are computed. A softmax-normalized distribution is then generated based on the computed similarity. The distribution thus takes advantage of property of the global structure of the queue. CROMDA then aligns the two distributions and learns a modal invariant global representation. In this way, CROMDA is able to obtain invariant property in each modality, where images with similar text representations should be similar and vice versa.
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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.
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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
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.
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公开(公告)号：US11495011B2
公开(公告)日：2022-11-08
申请号：US16988536
申请日：2020-08-07
Applicant: salesforce.com, inc.
Inventor： Shu Zhang , Chetan Ramaiah , Ran Xu , Caiming Xiong
Abstract: The system has a form analysis module that receives an image of a form into which values have been filled for the possible fields of information on the form, such as first name, address, age, and the like. By using a library of form templates, a form analysis module allows both flexibility of form processing and simplicity for the user. That is, the techniques used by the form analysis module allow the processing of any form image for which the library has a form template example. The form image need not precisely match any form template, but rather may be scaled or shifted relative to a corresponding template. Additionally, the user need only provide the form image itself, without providing any additional exemplars, metadata for training, or the like.
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公开(公告)号：US11238314B2
公开(公告)日：2022-02-01
申请号：US16686051
申请日：2019-11-15
Applicant: salesforce.com, inc.
Inventor： Ankit Chadha , Caiming Xiong , Ran Xu
Abstract: Computing systems may support image classification and image detection services, and these services may utilize object detection/image classification machine learning models. The described techniques provide for normalization of confidence scores corresponding to manipulated target images and for non-max suppression within the range of confidence scores for manipulated images. In one example, the techniques provide for generating different scales of a test image, and the system performs normalization of confidence scores corresponding to each scaled image and non-max suppression per scaled image These techniques may be used to provide more accurate image detection (e.g., object detection and/or image classification) and may be used with models that are not trained on modified image sets. The model may be trained on a standard (e.g. non-manipulated) image set but used with manipulated target images and the described techniques to provide accurate object detection.
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