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    AUTOMATED IMAGE RETRIEVAL WITH IMAGE GRAPH
    2.
    发明申请
    AUTOMATED IMAGE RETRIEVAL WITH IMAGE GRAPH 审中-公开

    公开(公告)号：US20200159766A1

    公开(公告)日：2020-05-21

    申请号：US16592006

    申请日：2019-10-03

    Applicant: THE TORONTO-DOMINION BANK

    Inventor： Maksims Volkovs ,  Cheng Chang ,  Guangwei Yu ,  Chundi Liu

    IPC: G06F16/583 ,  G06F16/903 ,  G06F16/9032 ,  G06F16/901 ,  G06F16/58

    Abstract: An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.

    Automated image retrieval with graph neural network
    4.
    发明授权
    Automated image retrieval with graph neural network 有权

    公开(公告)号：US11809486B2

    公开(公告)日：2023-11-07

    申请号：US17900530

    申请日：2022-08-31

    Applicant: THE TORONTO-DOMINION BANK

    Inventor： Chundi Liu ,  Guangwei Yu ,  Maksims Volkovs

    IPC: G06F16/58 ,  G06N3/08 ,  G06N3/04

    CPC classification number: G06F16/58 ,  G06N3/04 ,  G06N3/08

    Abstract: A content retrieval system uses a graph neural network architecture to determine images relevant to an image designated in a query. The graph neural network learns a new descriptor space that can be used to map images in the repository to image descriptors and the query image to a query descriptor. The image descriptors characterize the images in the repository as vectors in the descriptor space, and the query descriptor characterizes the query image as a vector in the descriptor space. The content retrieval system obtains the query result by identifying a set of relevant images associated with image descriptors having above a similarity threshold with the query descriptor.

    AUTOMATED IMAGE RETRIEVAL WITH GRAPH NEURAL NETWORK
    5.
    发明申请
    AUTOMATED IMAGE RETRIEVAL WITH GRAPH NEURAL NETWORK 有权

    公开(公告)号：US20220414145A1

    公开(公告)日：2022-12-29

    申请号：US17900530

    申请日：2022-08-31

    Applicant: THE TORONTO-DOMINION BANK

    Inventor： Chundi Liu ,  Guangwei Yu ,  Maksims Volkovs

    IPC: G06F16/58 ,  G06N3/08 ,  G06N3/04

    Abstract: A content retrieval system uses a graph neural network architecture to determine images relevant to an image designated in a query. The graph neural network learns a new descriptor space that can be used to map images in the repository to image descriptors and the query image to a query descriptor. The image descriptors characterize the images in the repository as vectors in the descriptor space, and the query descriptor characterizes the query image as a vector in the descriptor space. The content retrieval system obtains the query result by identifying a set of relevant images associated with image descriptors having above a similarity threshold with the query descriptor.

    TABULAR DATA GENERATION
    6.
    发明申请
    TABULAR DATA GENERATION 有权

    公开(公告)号：US20250124220A1

    公开(公告)日：2025-04-17

    申请号：US18911044

    申请日：2024-10-09

    Applicant: THE TORONTO-DOMINION BANK

    Inventor： Guangwei Yu ,  Junwei Ma ,  Anthony Lawrence Caterini ,  George Frazer Stein

    IPC: G06F40/177

    Abstract: A tabular data model, which may be pre-trained on a different data set, is used to generate data samples for a target class with a given set of context data points. The tabular data model is trained to predict class membership of a given data point with a set of context data points. Rather than use the predicted class directly, the class predictions are used to determine a class-conditional energy for a synthetic data point with respect to the target class. The synthetic data point may then be updated based on the class-conditional energy with a stochastic update algorithm, such as stochastic gradient Langevin dynamics or Adaptive Moment Estimation with noise. The value of the synthetic data point is sampled as a data point for the target class. This permits effective data augmentation for tabular data for downstream models.

    Weakly supervised action selection learning in video
    7.
    发明授权
    Weakly supervised action selection learning in video 有权

    公开(公告)号：US12211274B2

    公开(公告)日：2025-01-28

    申请号：US17716996

    申请日：2022-04-08

    Applicant: The Toronto-Dominion Bank

    Inventor： Junwei Ma ,  Satya Krishna Gorti ,  Maksims Volkovs ,  Guangwei Yu

    IPC: G06V20/40 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G06V20/70

    Abstract: A video localization system localizes actions in videos based on a classification model and an actionness model. The classification model is trained to make predictions of which segments of a video depict an action and to classify the actions in the segments. The actionness model predicts whether any action is occurring in each segment, rather than predicting a particular type of action. This reduces the likelihood that the video localization system over-relies on contextual information in localizing actions in video. Furthermore, the classification model and the actionness model are trained based on weakly-labeled data, thereby reducing the cost and time required to generate training data for the video localization system.

    TEXT-CONDITIONED VIDEO REPRESENTATION
    10.
    发明公开
    TEXT-CONDITIONED VIDEO REPRESENTATION 审中-公开

    公开(公告)号：US20230351753A1

    公开(公告)日：2023-11-02

    申请号：US17894738

    申请日：2022-08-24

    Applicant: THE TORONTO-DOMINION BANK

    Inventor： Satya Krishna Gorti ,  Junwei Ma ,  Guangwei Yu ,  Maksims Volkovs ,  Keyvan Golestan Irani ,  Noël Vouitsis

    IPC: G06V20/40

    CPC classification number: G06V20/47 ,  G06V20/41

    Abstract: A text-video recommendation model determines relevance of a text to a video in a text-video pair (e.g., as a relevance score) with a text embedding and a text-conditioned video embedding. The text-conditioned video embedding is a representation of the video used for evaluating the relevance of the video to the text, where the representation itself is a function of the text it is evaluated for. As such, the input text may be used to weigh or attend to different frames of the video in determining the text-conditioned video embedding. The representation of the video may thus differ for different input texts for comparison. The text-conditioned video embedding may be determined in various ways, such as with a set of the most-similar frames to the input text (the top-k frames) or may be based on an attention function based on query, key, and value projections.

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