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A comprehensive survey examining image classification into high-level and abstract categories, clarifying the tacit understanding of high-level semantics in computer vision through multidisciplinary analysis of commonsense, emotional, aesthetic, and interpretative semantics. The paper identifies persistent challenges in abstract concept image classification and emphasizes the importance of hybrid AI systems for addressing complex visual reasoning tasks.
Researchers study how adversarial robustness transfers across different perturbation types in deep neural networks, evaluating 32 attacks of 5 types on ImageNet models. Results show that robustness to one perturbation type doesn't always transfer to others and may sometimes hurt robustness elsewhere.