DEEP REINFORCEMENT LEARNING FRAMEWORK FOR SEQUENCE LEVEL PREDICTION OF HIGH DIMENSIONAL DATA

    公开(公告)号:US20220327828A1

    公开(公告)日:2022-10-13

    申请号:US17852602

    申请日:2022-06-29

    Abstract: In sequence level prediction of a sequence of frames of high dimensional data one or more affective labels are provided at the end of the sequence. Each label pertains to the entire sequence of frames. An action is taken with an agent controlled by a machine learning algorithm for a current frame of the sequence at a current time step. An output of the action represents affective label prediction for the frame at the current time step. A pool of actions taken up until the current time step including the action taken with the agent is transformed into a predicted affective history for a subsequent time step. A reward is generated on predicted actions up to the current time step by comparing the predicted actions against corresponding annotated affective labels.

    System and method for converting image data into a natural language description

    公开(公告)号:US11281709B2

    公开(公告)日:2022-03-22

    申请号:US16941299

    申请日:2020-07-28

    Abstract: For image captioning such as for computer game images or other images, bottom-up attention is combined with top-down attention to provide a multi-level residual attention-based image captioning model. A residual attention mechanism is first applied in the Faster R-CNN network to learn better feature representations for each region by taking spatial information into consideration. In the image captioning network, taking the extracted regional features as input, a second residual attention network is implemented to fuse the regional features attentionally for subsequent caption generation.

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