URVIS 2026 is the first challenge on adverse-to-extreme panoptic segmentation, drawing 17 participants and 47 submissions while introducing weighted Panoptic Quality as the ranking metric on the MUSES dataset.
Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform.Patterns, 3(7):100543
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
The LoViF 2026 Challenge creates the SeIQA dataset and benchmark for human-oriented semantic image quality assessment, with six submitted solutions reaching state-of-the-art performance.
The NTIRE 2026 challenge finds that large foundation models combined with ensembles and degradation-aware training produce the most robust deepfake detectors.
citing papers explorer
-
Adverse-to-the-eXtreme Panoptic Segmentation: URVIS 2026 Study and Benchmark
URVIS 2026 is the first challenge on adverse-to-extreme panoptic segmentation, drawing 17 participants and 47 submissions while introducing weighted Panoptic Quality as the ranking metric on the MUSES dataset.
-
LoViF 2026 Challenge on Human-oriented Semantic Image Quality Assessment: Methods and Results
The LoViF 2026 Challenge creates the SeIQA dataset and benchmark for human-oriented semantic image quality assessment, with six submitted solutions reaching state-of-the-art performance.
-
Robust Deepfake Detection, NTIRE 2026 Challenge: Report
The NTIRE 2026 challenge finds that large foundation models combined with ensembles and degradation-aware training produce the most robust deepfake detectors.