🤖 AI Summary
This study systematically investigates how uncertainty estimation in segmentation tasks is influenced by multiple factors, including dataset difficulty, model architecture, and temporal information. We introduce the first multivariate evaluation framework tailored for semantic and panoptic segmentation, comprehensively analyzing the impact of different backbone networks, datasets, and downstream task configurations on the quality of uncertainty estimates derived from deterministic models, ensemble methods, and temporal ensembles. Our experiments reveal that uncertainty calibration is notably more challenging in panoptic segmentation; temporal information improves calibration only under specific conditions; while sample diversity offers limited gains over simple baselines, ensemble methods—when properly deployed—consistently outperform deterministic approaches. This work establishes a systematic benchmark and provides practical guidance for uncertainty modeling in segmentation tasks.
📝 Abstract
In this study, we explore in depth a few under-studied topics at the intersection of uncertainty estimation and segmentation. Prior work has shown that the quality of uncertainty estimates can be very sensitive to a range of variables. As one of the main uses of uncertainty estimation is to help identify and deal with prediction errors in practical scenarios, any factors that affect this must be clearly identified. For example, do more challenging domains or different datasets and architectures result in worse performance when using uncertainty estimates? Can prior frames in a video sequence in fact provide useful uncertainty estimates comparable to other approaches? Is it possible to combine uncertainty estimation approaches, taking advantage of sample diversity, to get better estimates? Finally, when might it make sense to use an ensemble-based uncertainty estimate over a deterministic network? We address these questions by creating a framework for and executing a large scale study across many variables such as datasets, backbones, and downstream tasks, for both semantic and panoptic segmentation. We find that a) the more challenging task of panoptic segmentation usually results in worse performance while high performance variance between datasets and backbones indicates that generalization is not guaranteed, b) time series samples can be useful for specific configurations, but in many cases are not worth the cost, c) sample diversity shows the most promise in the downstream task of calibration, but otherwise fails to beat simpler alternatives, d) a deterministic approach is adequate for some downstream tasks, but ensembles allow for significant improvements if the right conditions can be achieved in deployment.