Retrocausal, Inc.

Sateesh Kumar

Andrey Konin

Muhammad Zeeshan Zia

Quoc-Huy Tran

Sanjay Haresh

Shahram Najam Syed

A system and method for learning human activities from video demonstrations using video augmentation is disclosed. The method includes receiving original videos from one or more data sources. The method includes processing the received original videos using one or more video augmentation techniques to generate a set of augmented videos. Further, the method includes generating a set of training videos by combining the received original videos with the generated set of augmented videos. Also, the method includes generating a deep learning model for the received original videos based on the generated set of training videos. Further, the method includes learning the one or more human activities performed in the received original videos by deploying the generated deep learning model. The method includes outputting the learnt one or more human activities performed in the original videos.

System and method for learning human activities from video demonstrations using video augmentation

A system and method for correlating video frames in a computing environment. The method includes receiving first video data and second video data from one or more data sources. The method further includes encoding the received first video data and the second video data using machine learning network. Further, the method includes generating first embedding video data and second embedding video data corresponding to the received first video data and the received second video data. Additionally, the method includes determining a contrastive IDM temporal regularization value for the first video data and the second video data. The method further includes determining temporal alignment loss between the first video data and the second video data. Also, the method includes determining correlated video frames between the first video data and the second video databased on the determined temporal alignment loss and the determined contrastive IDM temporal regularization value.

System and method for correlating video frames in a computing environment

A system for building computational models of a goal-driven task from demonstration is disclosed. A task recording subsystem receives a recorded video file or recorded sensor data representative of an expert demonstration for a task. An instructor authoring tool generates one or more sub-activity proposals; enables an instructor to specify one or more sub-activity labels upon modification of the one or more sub-activity proposals into one or more sub-tasks. A task learning subsystem learns the one or more sub-tasks represented in the demonstration of the task; builds an activity model to predict and locate the task being performed in the recorded video file. A task evaluation subsystem evaluates a live video representative of the task; generates at least one performance description statistics; identifies a type of activity step executed by the one or more actors; provides an activity guidance feedback in real-time to the one or more actors.

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Karachi, Pakistan

Sateesh Kumar