Abstract: In order to implement the foregoing object, an exemplary embodiment of the present disclosure discloses a video retrieval method performed by a computing device. The video retrieval method may include: generating one or more sub video data based on one or more video data; identifying, by a machine learning enabled key frame detecting module having one or more encoders, key frame information based on one or more encoded vectors generated from the one or more sub video data; and segmenting the one or more video data into one or more retrieval video data based on the identified key frame information.

Abstract: In order to implement the foregoing object, an exemplary embodiment of the present disclosure discloses a video retrieval method performed by a computing device. The video retrieval method may include: receiving retrieval query data for one or more video data; generating a retrieval query vector based on the received retrieval query data; comparing the retrieval query vector with a plurality of video retrieval vectors each representing target retrieval video data segmented based on key frame information; and selecting one or more target retrieval video data by the result of the comparison between the retrieval query vector and the plurality of video retrieval vectors.

Abstract: A video analysis system performs indexing of one or more videos in parallel by using pipelines executed by task processors. A task processor may include compute resources configured on a cloud infrastructure or on-premise compute resources. The video analysis system receives requests to index one or more videos. In one instance, the requests may be from users of client devices with requests to index the videos, such that the indexed information can be used to perform downstream applications, such as search query-based retrieval, and the like. The video analysis system performs the indexing process in parallel, so that significant bottlenecks can be eliminated compared to existing methods.

Abstract: A video analysis system performs indexing of one or more videos in parallel by using pipelines executed by task processors. A task processor may include compute resources configured on a cloud infrastructure or on-premise compute resources. The video analysis system receives requests to index one or more videos. In one instance, the requests may be from users of client devices with requests to index the videos, such that the indexed information can be used to perform downstream applications, such as search query-based retrieval, and the like. The video analysis system performs the indexing process in parallel, so that significant bottlenecks can be eliminated compared to existing methods.

Abstract: In order to implement the foregoing object, an exemplary embodiment of the present disclosure discloses a video retrieval method performed by a computing device. The video retrieval method may include: segmenting one or more video data into two or more unit video data; encoding, by one or more encoders comprised in a machine learning enabled key frame detection module, two or more unit video data; generating one or more key frame detection vectors for each of the two or more unit video data based on the result of the encoding; and generating feature vector of one or more retrieval video data based on a combination of one or more vectors among the key frame detection vectors.

Abstract: In order to implement the foregoing object, an exemplary embodiment of the present disclosure discloses a video retrieval method performed by a computing device. The video retrieval method may include: segmenting one or more video data into two or more unit video data; encoding, by one or more encoders comprised in a machine learning enabled key frame detection module, two or more unit video data; generating one or more key frame detection vectors for each of the two or more unit video data based on the result of the encoding; and generating feature vector of one or more retrieval video data based on a combination of one or more vectors among the key frame detection vectors.

Abstract: A video analysis system trains and uses a machine-learned adaptive thresholding model configured to receive a query (e.g., text, image, video), and generate a predicted threshold value for a corresponding video retrieval model that indicates what value the filtering threshold should be for the query. The video analysis system filters video segments for the query that are associated with relevance scores above the predicted threshold generated by the adaptive thresholding model. In one instance, the adaptive thresholding model is configured as a machine-learned model, including neural networks, embedding models, transformer-based architectures, and the like that are capable of generating a predicted threshold value given a query that includes, for example, text, images, videos, audio, or any other appropriate data modality.

Abstract: In order to implement the foregoing object, an exemplary embodiment of the present disclosure discloses a video retrieval method performed by a computing device. The video retrieval method may include: receiving retrieval query data for one or more video data; generating a retrieval query vector based on the received retrieval query data; comparing the retrieval query vector with a plurality of video retrieval vectors each representing target retrieval video data segmented based on key frame information; and selecting one or more target retrieval video data by the result of the comparison between the retrieval query vector and the plurality of video retrieval vectors.

Abstract: Provided is a method of providing a user interface for video retrieval performed by a computing device, the method including: receiving a search query, providing a search result interface displaying at least one search video content and search section searched by the search query, wherein the search video content and the search section are related with at least one of a plurality of video semantic search attributes corresponding to the search query, and providing a timeline view interface for the search video content when it is determined to display the search result interface according to a timeline view mode, wherein the timeline view interface has a visual characteristic indicating the degree of relevance to at least one of the plurality of video semantic search attributes corresponding to the search query.

Abstract: In order to implement the foregoing object, an exemplary embodiment of the present disclosure discloses a video retrieval method performed by a computing device. The video retrieval method may include: receiving retrieval query data for one or more video data; generating a retrieval query vector based on the received retrieval query data; comparing the retrieval query vector with a plurality of video retrieval vectors each representing target retrieval video data segmented based on key frame information; and selecting one or more target retrieval video data by the result of the comparison between the retrieval query vector and the plurality of video retrieval vectors.

Abstract: Provided is a method of providing a user interface for video retrieval performed by a computing device, the method including: receiving a search query, providing a search result interface displaying at least one search video content and search section searched by the search query, wherein the search video content and the search section are related with at least one of a plurality of video semantic search attributes corresponding to the search query, and providing a timeline view interface for the search video content when it is determined to display the search result interface according to a timeline view mode, wherein the timeline view interface has a visual characteristic indicating the degree of relevance to at least one of the plurality of video semantic search attributes corresponding to the search query.

Abstract: Provided is a method of providing a user interface for video retrieval performed by a computing device, the method including: receiving a search query, providing a search result interface displaying at least one search video content and search section searched by the search query, wherein the search video content and the search section are related with at least one of a plurality of video semantic search attributes corresponding to the search query, and providing a timeline view interface for the search video content when it is determined to display the search result interface according to a timeline view mode, wherein the timeline view interface has a visual characteristic indicating the degree of relevance to at least one of the plurality of video semantic search attributes corresponding to the search query.

Abstract: In order to implement the foregoing object, an exemplary embodiment of the present disclosure discloses a video retrieval method performed by a computing device. The video retrieval method may include: segmenting one or more video data into two or more unit video data; encoding, by one or more encoders comprised in a machine learning enabled key frame detection module, two or more unit video data; generating one or more key frame detection vectors for each of the two or more unit video data based on the result of the encoding; and generating feature vector of one or more retrieval video data based on a combination of one or more vectors among the key frame detection vectors.

Abstract: In order to implement the foregoing object, an exemplary embodiment of the present disclosure discloses a video retrieval method performed by a computing device. The video retrieval method may include: generating one or more sub video data based on one or more video data; identifying, by a machine learning enabled key frame detecting module having one or more encoders, key frame information based on one or more encoded vectors generated from the one or more sub video data; and segmenting the one or more video data into one or more retrieval video data based on the identified key frame information.

Abstract: In order to implement the foregoing object, an exemplary embodiment of the present disclosure discloses a video retrieval method performed by a computing device. The video retrieval method may include: receiving retrieval query data for one or more video data; generating a retrieval query vector based on the received retrieval query data; comparing the retrieval query vector with a plurality of video retrieval vectors each representing target retrieval video data segmented based on key frame information; and selecting one or more target retrieval video data by the result of the comparison between the retrieval query vector and the plurality of video retrieval vectors.

Abstract: In order to implement the foregoing object, an exemplary embodiment of the present disclosure discloses a video retrieval method performed by a computing device. The video retrieval method may include: identifying, by a machine learning enabled key frame detecting module having one or more encoders, key frame information for one or more video data based on one or more encoded vectors respectively generated from one or more unit video data contained in the one or more video data; segmenting the one or more video data into one or more target retrieval video data based on the identified key frame information; and generating, by a machine learning enabled retrieval vector generating module having one or more encoders, one or more retrieval video vectors respectively representing the one or more target retrieval video data.