main webpage W Topic Sensor Fusion arXiv (Cornell University) Informative Data Selection with Uncertainty for Multi-modal Object Detection 2023 Noise has always been nonnegligible trouble in object detection by creating confusion in model reasoning, thereby reducing the informativeness of the data. It can lead to inaccurate recognition due to the shift in the observed pattern, that requires a robust … Article Open Sensor Fusion Open Article Page

Sensor Fusion

Combining of sensor data from disparate sources Sensor fusion is the process of combining sensor data or data derived from disparate sources such that the resulting information has less uncertainty than would be possible when these sources were used individually. For instance, one could potentially obtain a more accurate location estimate of an indoor object by combining multiple data sources such as video cameras and WiFi localization signals. The term uncertainty reduction in this case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view, such as stereoscopic vision (calculation of depth information by combining two-dimensional images from two cameras at slightly different viewpoints). Critical Symbolic Virtual Narrative Open Sensor Fusion Open Article Page

Exploring foci of: arXiv (Cornell University) Informative Data Selection with Uncertainty for Multi-modal Object Detection 2023 Noise has always been nonnegligible trouble in object detection by creating confusion in model reasoning, thereby reducing the informativeness of the data. It can lead to inaccurate recognition due to the shift in the observed pattern, that requires a robust generalization of the models. To implement a general vision model, we need to develop deep learning models that can adaptively select valid information from multi-modal data. This is mainly based on two reasons. Multi-modal learning can break through the inher… Open Article Page

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Explore Sensor Fusion W Topic Sensor Fusion arXiv (Cornell University) Informative Data Selection with Uncertainty for Multi-modal Object Detection 2023 Noise has always been nonnegligible trouble in object detection by creating confusion in model reasoning, thereby reducing the informativeness of the data. It can lead to inaccurate recognition due to the shift in the observed pattern, that requires a robust … Article Critical Symbolic Virtual Narrative Open Sensor Fusion Open Article Page