Using Informative AI to Understand Camouflaged Object Detection and Segmentation

Explainable artificial intelligence is crucial for trust-building and gaining insights into machine learning applications, allowing for the identification of improvement opportunities. Recent advances in computer vision have incorporated XAI into object detection, providing explanations to validate system predictions. However, informative artificial intelligence, which focuses on understanding the underlying world behind data, has not been integrated into camouflaged object detection and segmentation research. We propose leveraging the Self-Explaining Decision Architecture alongside localization and ranking techniques to bridge this gap. By combining explainable artificial intelligence with informative artificial intelligence concepts into camouflaged object detection and segmentation, we strive to deepen our understanding of visual cues that undermine camouflage.