External Influence
External influence, encompassing how various factors impact systems and processes, is a burgeoning research area with applications across diverse fields. Current studies focus on quantifying and mitigating the influence of biases in large language models, noise in image processing, and user preferences in recommender systems, often employing techniques like deep learning, Gaussian filtering, and stochastic simulation. Understanding and controlling external influences is crucial for developing robust, fair, and reliable AI systems and improving the accuracy and efficiency of various technologies, from medical image analysis to autonomous vehicles.
Papers
Influence of Water Droplet Contamination for Transparency Segmentation
Volker Knauthe, Paul Weitz, Thomas Pöllabauer, Tristan Wirth, Arne Rak, Arjan Kuijper, Dieter W. Fellner
Predicting the Influence of Adverse Weather on Pedestrian Detection with Automotive Radar and Lidar Sensors
Daniel Weihmayr, Fatih Sezgin, Leon Tolksdorf, Christian Birkner, Reza N. Jazar