Other

Amazon Technologies, Inc.

Videoken, Inc.

Ankit Gandhi

Arijit Biswas

Om Dadaji Deshmukh

Sumit Negi

Vineet Shashikant Chaoji

Anil Raghavendrachar Yelundur

Kuldeep Yadav

Sonal S Patil

Ranjeet Kumar

Sovan Biswas

Sanket Sanjay Barhate

Sahil Loomba

Respective initial feature sets are obtained for the nodes of a graph in which the nodes represent instances of entity types and edges represent relationships. Using the initial feature sets and the graph, a graph convolutional model is trained to generate one or more types of predictions. In the model, a representation of a particular node at a particular hidden layer is based on aggregated representations of neighbor nodes, and an embedding produced at a final hidden layer is used as input to a prediction layer. The trained model is stored.

Artificial intelligence system employing graph convolutional networks for analyzing multi-entity-type multi-relational data

A method and a system are provided for segmenting a multimedia content. The method estimates a count of a plurality of multimedia segments in the multimedia content, and a duration of each of the plurality of multimedia segments in the multimedia content. The method determines a cost function associated with a multimedia segment from the plurality of multimedia segments, based on the count of the plurality of multimedia segments, and the duration of each of the plurality of multimedia segments. The method further determines an updated count of the plurality of multimedia segments, and an updated duration of each of the plurality of multimedia segments until the cost function satisfies a pre-defined criteria. Based on the updated count of the plurality of multimedia segments, and the updated duration of each of the plurality of multimedia segments, the method segments the multimedia content into the plurality of multimedia segments.

Methods and systems for segmenting multimedia content

The disclosed embodiments illustrate methods of generation of a table of content by processing multimedia content. The method includes identifying a set of key-phrases from the multimedia content based on one or more external data sources. The method further includes determining one or more segments of the multimedia content, based on the identified set of key-phrases, wherein a segment of the determined one or more segments comprises a subset of key-phrases from the set of key-phrases. The method further includes selecting at least a key-phrase from the subset of key-phrases of each of the corresponding one or more segments. The method further includes generating the table of content based on the selected key-phrase from each of the one or more segments, wherein the selected key-phrase from each of the one or more segments in the generated table of content is utilized to navigate through the multimedia content.

Method and system for generation of a table of content by processing multimedia content

Features are extracted from visual and audio modalities of a video to infer the location of figures/tables/equations/graphs/flow-charts determined as video anchor points which are highlighted on the video timeline to enable quick navigation and provide a quick summary of the video. A voice-based mechanism navigates to a point-of-interest in the video. In case of bandwidth-constrained settings, videos are often played at a very low resolution (quality), and often users need to increase video resolution manually to understand content presented in the figures. Using the automatic identification of these aforementioned anchored points, the resolution can be changed dynamically during streaming a video, which will provide a better viewing experience.

System and methods to create multi-faceted index instructional videos

The disclosed embodiments illustrate method and system of processing multimedia content to generate a text transcript. The method includes segmenting each of a set of text frames to determine spatial regions. The method further includes extracting one or more keywords from each of the determined spatial regions. The method further includes determining the first set of keywords from the extracted one or more keywords based on filtering of one or more off-topic keywords from the extracted one or more keywords. The method further includes extracting a second set of keywords based on the determined first set of keywords. The method further includes generating a graph between each of a first set of keywords and one or more of a second set of keywords. The method further includes dynamically generating the text transcript of audio content in the multimedia content based on the generated graph.

Method and system for processing multimedia content to dynamically generate text transcript

A method for detecting one or more topic transitions in a multimedia content includes identifying, one or more frames from a plurality of frames of the multimedia content based on a comparison between one or more content items in a first frame of the plurality of frames, and the one or more content items in a first set of frames of the plurality of frames. The method further includes determining at least a first score, and a second score for each of the one or more frames. Additionally, the method includes determining a likelihood for each of the one or more frames based at least on the first score, and the second score, wherein the likelihood is indicative of a topic transition among the one or more frames.

Methods and systems for detecting topic transitions in a multimedia content

Embodiments disclose methods, systems and non-transitory computer readable medium for joint temporal segmentation and classification of user activities in an egocentric video. The method includes extracting low-level features from a live dataset based on predefined feature categories; determining at least one activity change frame from the egocentric video frames based on the extracted features; dividing the live dataset into partitions based on the activity change frame, each partition begins with a candidate frame; computing a recursive cost function at the candidate frame of each partition based on dynamic programming; determining a beginning time instant of the candidate frame based on the computation; segmenting the live dataset into multiple segments based on the determined time instant; identifying at least one activity segment that corresponds a user activity using a trained activity model based on multiple instance learning approach; and simultaneously associating a predefined activity label with the identified activity segment.

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Raipur, India

Ankit Gandhi