3 accepted papers
Achieving robustness in image segmentation models is challenging due to the fine-grained nature of pixel-level classification. These models, which are crucial for many real-time perception applications, particularly struggle when faced with natural corruptions in the wild for autonomous systems. Whi…
Long-duration, off-road, autonomous missions require robots to continuously perceive their surroundings regardless of the ambient lighting conditions. Most existing autonomy systems heavily rely on active sensing, e.g., LiDAR, RADAR, and Time-of-Flight sensors, or use (stereo) visible light imaging
We present a novel unsupervised obstacle segmentation architecture that follows a novel Relation Distillation (RD) paradigm. Our architecture design was inspired by a self-supervised teacher-student approach that relies on the Semantic Distillation originally devised for representation learning. Whi…