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Uncertainty-Aware Panoptic Segmentation

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하위 태스크: 0
평가표: 1
보존된 평가 기록: 1

The ternary difficulty levels assigned to each pixel during the annotation of MUSES enable a novel task: uncertainty-aware panoptic segmen- tation. In this task, a panoptic segmentation model is compensated for errors in difficult image regions if it predicts the difficulty level correctly. To incorporate this idea into evaluation, we introduce the uncertainty-aware panoptic quality (UPQ) metric, an extension of panoptic quality.

데이터셋

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MUSES: MUlti-SEnsor Semantic perception dataset

MUSES offers 2500 multi-modal scenes, evenly distributed across various combinations of weather conditions (clear, fog, rain, and snow) and types of illumination (daytime, nighttime). Each image includes high-quality 2D pixel-level panoptic annotations and class-level and novel instance-level uncertainty annotations. Further, each adverse-condition image has a corresponding image of the same scene taken under clear-weather, daytime conditions. The annotation process for MUSES utilizes all available sensor data, allowing the annotators to also reliably label degraded image regions that are still discernible in other modalities. This results in better pixel coverage in the annotations and creates a more challenging evaluation setup. The dataset provides public benchmarks for: - Panoptic segmentation - Uncertainty-aware panoptic segmentation - Semantic segmentation - Object detection Sensor modalities included: - Frame camera (RGB) - MEMS lidar - FMCW radar - HD event camera - IMU/GNSS sensor

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.