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    • 85. 发明专利
    • Utilizing object attribute detection models to automatically select instances of determined objects in images
    • GB202005714D0
    • 2020-06-03
    • GB202005714
    • 2020-04-20
    • ADOBE INC
    • The present disclosure relates to an object selection system that accurately detects and automatically selects user-requested objects (e.g., query objects) in a digital image. For example, the object selection system builds and utilizes an object selection pipeline to determine which object detection neural network to utilize to detect a query object based on analysing the object class of the query object. In addition, the object selection system can add, update, or replace portions of the object selection pipeline to improve overall accuracy and efficiency of automatic object selection within an image. The application identifies from a selection query an object to be selected in a digital image then selects, based on an analysis of the query object, a first object detection neural network from a plurality of object detection neural networks such a plurality comprising a specialist object detection neural network, a concept-based object detection neural network, a known object class detection neural network and an unknown object class detection neural network. The selected neural network then detects the query object in the image from which a first object mask of the query object is generated using an object mask neural network and the image with object mask is provided in response to the selection query.
    • 89. 发明专利
    • NON-LOCAL MEMORY NETWORK FOR SEMI-SUPERVISED VIDEO OBJECT SEGMENTATION
    • AU2019213369A1
    • 2020-04-30
    • AU2019213369
    • 2019-08-07
    • ADOBE INC
    • LEE JOON-YOUNGXU NINGOH SEOUNGWUG
    • G06T7/10G06K9/46
    • Abstract Certain aspects involve using a space-time memory network to locate one or more target objects in video content for segmentation or other object classification. In one example, a video editor generates a query key map and a query value map by applying a space-time memory network to features of a query frame from video content. The video editor retrieves a memory key map and a memory value map that are computed, with the space-time memory network, from a set of memory frames from the video content. The video editor computes memory weights by applying a similarity function to the memory key map and the query key map. The video editor classifies content in the query frame as depicting the target feature using a weighted summation that includes the memory weights applied to memory locations in the memory value map. 200 ,, 202 Access a queryframe Generate a query key map and a Generate memory key maps and 204'e query value map from features of the 205 memory value maps for memory query frame frames 206 Retrieve memory key maps and memory value maps from a memory network Compute memory weights by applying a similarity function to the memory key 208 maps and the query key map Generate a segmentation mask for the query frame from a weighted 210 summation that includes the memory weights applied to the memory value maps