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    GENERATING IMAGE OBJECT SEGMENTATIONS UTILIZING GRAPH-CUT PARTITIONING IN SELF-SUPERVISED OBJECT DISCOVERY
    1.
    发明申请
    GENERATING IMAGE OBJECT SEGMENTATIONS UTILIZING GRAPH-CUT PARTITIONING IN SELF-SUPERVISED OBJECT DISCOVERY 有权

    公开(公告)号：US20250111520A1

    公开(公告)日：2025-04-03

    申请号：US18478093

    申请日：2023-09-29

    Applicant: Adobe Inc.

    Inventor： Silky Singh ,  Shripad Vilasrao Deshmukh ,  Mausoom Sarkar ,  Balaji Krishnamurthy

    IPC: G06T7/194 ,  G06T7/162 ,  G06T7/246

    Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media that provide self-supervised object discovery systems that combine motion and appearance information to generate segmentation masks from a digital image or digital video and delineate one or more salient objects within the digital image/digital video. The disclosed systems utilize a neural network encoder to generate a fully connected graph based on image patches from the digital input, incorporating image patch feature and optical flow patch feature similarities to produce edge weights. The disclosed systems partition the generated graph to produce a segmentation mask. Furthermore, the disclosed systems iteratively train a segmentation network based on the segmentation mask as a pseudo-ground truth via a bootstrapped, self-training process. By utilizing both motion and appearance information to generate a bi-partitioned graph, the disclosed systems produce high-quality object segmentation masks that represent a foreground and background of digital inputs.

    PERSONALIZED FORM ERROR CORRECTION PROPAGATION
    3.
    发明公开
    PERSONALIZED FORM ERROR CORRECTION PROPAGATION 审中-公开

    公开(公告)号：US20240362941A1

    公开(公告)日：2024-10-31

    申请号：US18140143

    申请日：2023-04-27

    Applicant: Adobe Inc.

    Inventor： Silky Singh ,  Surgan Jandial ,  Shripad Vilasrao Deshmukh ,  Milan Aggarwal ,  Mausoom Sarkar ,  Balaji Krishnamurthy ,  Arneh Jain ,  Abhinav Java

    IPC: G06V30/262 ,  G06V30/14 ,  G06V30/19 ,  G06V30/414

    CPC classification number: G06V30/274 ,  G06V30/1444 ,  G06V30/19147 ,  G06V30/414

    Abstract: A corrective noise system receives an electronic version of a fillable form generated by a segmentation network and receives a correction to a segmentation error in the electronic version of the fillable form. The corrective noise system is trained to generate noise that represents the correction and superimpose the noise on the fillable form. The corrective noise system is further trained to identify regions in a corpus of forms that are semantically similar to a region that was subject to the correction. The generated noise is propagated to the semantically similar regions in the corpus of forms and the noisy corpus of forms is provided as input to the segmentation network. The noise causes the segmentation network to accurately identify fillable regions in the corpus of forms and output a segmented version of the corpus of forms having improved fidelity without retraining or otherwise modifying the segmentation network.

    FORM STRUCTURE SIMILARITY DETECTION
    4.
    发明公开
    FORM STRUCTURE SIMILARITY DETECTION 审中-公开

    公开(公告)号：US20240330351A1

    公开(公告)日：2024-10-03

    申请号：US18190686

    申请日：2023-03-27

    Applicant: Adobe Inc.

    Inventor： Abhinav Java ,  Surgan Jandial ,  Shripad Vilasrao Deshmukh ,  Milan Aggarwal ,  Mausoom Sarkar ,  Balaji Krishnamurthy ,  Arneh Jain

    IPC: G06F16/383 ,  G06F16/332 ,  G06V30/19 ,  G06V30/412

    CPC classification number: G06F16/383 ,  G06F16/332 ,  G06V30/19147 ,  G06V30/412

    Abstract: Form structure similarity detection techniques are described. A content processing system, for instance, receives a query snippet that depicts a query form structure. The content processing system generates a query layout string that includes semantic indicators to represent the query form structure and generates candidate layout strings that represent form structures from a target document. The content processing system calculates similarity scores between the query layout string and the candidate layout strings. Based on the similarity scores, the content processing system generates a target snippet for display that depicts a form structure that is structurally similar to the query form structure. The content processing system is further operable to generate a training dataset that includes image pairs of snippets depicting form structures that are structurally similar. The content processing system utilizes the training dataset to train a machine learning model to perform form structure similarity matching.

    TRAJECTORY-BASED EXPLAINABILITY FRAMEWORK FOR REINFORCEMENT LEARNING MODELS
    5.
    发明申请
    TRAJECTORY-BASED EXPLAINABILITY FRAMEWORK FOR REINFORCEMENT LEARNING MODELS 有权

    公开(公告)号：US20240403651A1

    公开(公告)日：2024-12-05

    申请号：US18328174

    申请日：2023-06-02

    Applicant: Adobe Inc.

    Inventor： Shripad Vilasrao Deshmukh ,  Arpan Dasgupta ,  Balaji Krishnamurthy ,  Chirag Agarwal ,  Georgios Theocharous ,  Jayakumar Subramanian

    IPC: G06N3/092

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that provide a trajectory-based explainability framework for reinforcement learning models. For example, the disclosed systems generate trajectory clusters from trajectories utilized to train a reinforcement learning agent. In some embodiments, the disclosed system generates a complementary target data set by removing a target trajectory cluster from the trajectory clusters. In some cases, the disclosed system trains a test reinforcement learning agent utilizing the complementary target data set and generates a cluster attribution by comparing the result of the test reinforcement learning agent with the result of the reinforcement learning agent.

    Form structure similarity detection
    6.
    发明授权
    Form structure similarity detection 有权

    公开(公告)号：US12124497B1

    公开(公告)日：2024-10-22

    申请号：US18190686

    申请日：2023-03-27

    Applicant: Adobe Inc.

    Inventor： Abhinav Java ,  Surgan Jandial ,  Shripad Vilasrao Deshmukh ,  Milan Aggarwal ,  Mausoom Sarkar ,  Balaji Krishnamurthy ,  Arneh Jain

    IPC: G06F16/383 ,  G06F16/332 ,  G06V30/19 ,  G06V30/412

    CPC classification number: G06F16/383 ,  G06F16/332 ,  G06V30/19147 ,  G06V30/412

    Abstract: Form structure similarity detection techniques are described. A content processing system, for instance, receives a query snippet that depicts a query form structure. The content processing system generates a query layout string that includes semantic indicators to represent the query form structure and generates candidate layout strings that represent form structures from a target document. The content processing system calculates similarity scores between the query layout string and the candidate layout strings. Based on the similarity scores, the content processing system generates a target snippet for display that depicts a form structure that is structurally similar to the query form structure. The content processing system is further operable to generate a training dataset that includes image pairs of snippets depicting form structures that are structurally similar. The content processing system utilizes the training dataset to train a machine learning model to perform form structure similarity matching.

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