Multi-Modal Geolocation Prediction from RGB-D Videos


Multi-Modal Geolocation Prediction from RGB-D Videos – A large body of recent work has shown that image data are strongly interdependent with semantic content. We propose a general framework for the analysis of semantic content within audio streams that incorporates image content and the semantic content in the input video signals. The proposed framework is applied to annotated audio streams to produce synthetically generated images. The annotated audio streams are then used to train a deep convolutional neural network (CNN) to predict semantic content at each channel. The CNNs learn to match semantic content, while simultaneously learning to predict semantic content in a domain of two images. Experimental results on multiple datasets, both single-view and mixed-view datasets demonstrate that the proposed framework outperforms several state-of-the-art models for the same task.

The recent advances in AI applications have proven to be highly successful. In this paper, we present a system that uses a human-generated video from a mobile phone to perform complex tasks such as action recognition and vision in a robotic arm, as a semi-supervised process. We train the robot to perform multiple, sequential, action-based tasks, based on the action set that human players perform on the video. These tasks are presented as a new feature from the video, which could be used as a proxy to measure cognitive activity. The video captured by the robot shows human players performing multiple actions and actions on different video frames, in order to assess the visual state of the agent. We show how in this way the robotic arm and our video can be integrated to a single, sequential action detection system. In particular, we show how to train an action-tracking system that aims to recognize the actions of each player as a sequence of action clusters. We analyze the results of both the robot and human tasks to demonstrate the effectiveness of the system.

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Multi-Modal Geolocation Prediction from RGB-D Videos

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    Dynamic Systems as a Multi-Agent SimulationThe recent advances in AI applications have proven to be highly successful. In this paper, we present a system that uses a human-generated video from a mobile phone to perform complex tasks such as action recognition and vision in a robotic arm, as a semi-supervised process. We train the robot to perform multiple, sequential, action-based tasks, based on the action set that human players perform on the video. These tasks are presented as a new feature from the video, which could be used as a proxy to measure cognitive activity. The video captured by the robot shows human players performing multiple actions and actions on different video frames, in order to assess the visual state of the agent. We show how in this way the robotic arm and our video can be integrated to a single, sequential action detection system. In particular, we show how to train an action-tracking system that aims to recognize the actions of each player as a sequence of action clusters. We analyze the results of both the robot and human tasks to demonstrate the effectiveness of the system.


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