Towards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection

ArXi:2506.21486v2 Announce Type: replace-cross Deep neural networks have set the state-of-the-art in computer vision tasks such as bounding box detection and semantic segmentation. Object detectors and segmentation models assign confidence scores to predictions, reflecting the model's uncertainty in object detection or pixel-wise classification. However, these confidence estimates are often miscalibrated, as their architectures and loss functions are tailored to task performance rather than probabilistic foundation.