CAVE : Detecting and Explaining Commonsense Anomalies in Visual Environments (2025.emnlp-main)
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Rishika Bhagwatkar, Syrielle Montariol, Angelika Romanou, Beatriz Borges, Irina Rish, Antoine Bosselut
| Challenge: | a new benchmark for computer vision fails to capture richness and unpredictability of real-world anomalies . state-of-the-art VLMs struggle with visual anomaly perception and commonsense reasoning . elucidating the nature of anomalies is a fundamental human trait . |
| Approach: | They propose a benchmark for visual anomalies that includes annotations for visual grounding and categorizing anomalies based on their visual manifestations, their complexity, severity, and commonness. |
| Outcome: | The proposed benchmark improves on existing vision models by incorporating visual annotations. |
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