Camera anomalies like rain or dust can severelydegrade image quality and its related tasks, such as localizationand segmentation. In this work we address this importantissue by implementing a pre-processing step that can effectivelymitigate such artifacts in a real-time fashion, thus supportingthe deployment of autonomous systems with limited computecapabilities. We propose a shallow generator with aggregation,trained in an adversarial setting to solve the ill-posed problemof reconstructing the occluded regions. We add an enhancer tofurther preserve high-frequency details and image colorization.We also produce one of the largest publicly available datasets1to train our architecture and use realistic synthetic raindrops toobtain an improved initialization of the model. We benchmarkour framework on existing datasets and on our own imagesobtaining state-of-the-art results while enabling real-time per-formance, with up to 40x faster inference time than existingapproaches.
Embodied Hazard Mitigation using Vision-Language Models for Autonomous Mobile Robots
Department of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University（机械与航空航天工程系，坦顿工程学院，纽约大学）；GenAuto.ai by General Autonomy Inc.（General Autonomy Inc. 的 GenAuto.ai）
See Something, Say Something: Context-Criticality-Aware Mobile Robot Communication for Hazard Mitigations
New York University Tandon School of Engineering（纽约大学坦登工程学院）；The City College of New York, Grove School of Engineering（纽约市立学院格罗夫工程学院）；ASAS LABS, Department of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University（纽约大学坦登工程学院机械与航空航天工程系ASAS实验室）
Toward Evidence-Driven Human-Agent-Robot Teaming for Earth-Independent Anomaly Triage
NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；SETI Institute（搜寻地外文明研究所）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Michigan（密歇根大学）；Repertory Robotics
Explainable Deep Anomaly Detection with Sequential Hypothesis Testing for Robotic Sewer Inspection
School of Electrical and Electronic Engineering（电气与电子工程学院）；University of Sheffield（谢菲尔德大学）；School of Mechanical, Aerospace and Civil Engineering（机械、航空航天与土木工程学院）
Enhanced Breast Cancer Tumor Classification using MobileNetV2: A Detailed Exploration on Image Intensity, Error Mitigation, and Streamlit-driven Real-time Deployment
Viterbi School of Engineering（维特比工程学院）；College of Engineering（工程学院）；University of Southern California（南加州大学）；Rochester Institute of Technology（罗切斯特理工学院）；Northeastern University（东北大学）；School of Arts & Sciences（艺术与科学学院）；State University of New York Plattsburgh（纽约州立大学普拉茨堡分校）
