Ads and Anomalies: Structuring the Known and Probing the Unknown

Keralapura Nagaraju Amruth Sagar

Abstract

The convergence of computer vision and advertising analysis has seen progress, but existing advertisement datasets remain limited. Many are small subsets of larger datasets, and while larger datasets may offer multiple annotations, they often lack consistent organization across all images, making it challenging to structure ads hierarchically. This lack of clear categorization and overlap in labeling hinders in-depth analysis. To address this, we introduce MAdVerse1 , a comprehensive, multilingual dataset of over 50,000 advertisements sourced from websites, social media, and e-newspapers. MAdVerse organizes ads into a hierarchy with 11 primary categories, 51 sub-categories, and 524 specific brands, facilitating fine-grained analysis across a diverse range of brands. We establish baseline performance metrics for key ad-related tasks, including hierarchical classification, source classification, and hierarchy induction in other ad datasets and, in a multilingual context, thereby providing a structured foundation for advertisement analysis.

In our second work, we investigate foundational aspects of out-of-distribution (OOD) detection. Existing OOD benchmarks typically focus on broad, class-level shifts but lack controlled environments for assessing how individual attribute changes such as color or shape affect OOD detection. To bridge this gap, we created two synthetic datasets, SHAPES and CHARS2 , each designed to allow controlled experimentation with isolated shifts in attributes. Through variations in color, size, rotation, and other factors, these datasets facilitate a targeted examination of OOD detection performance under specific conditions, providing insights into how OOD detection is affected under different attribute shifts. Later, we apply OOD detection methods to advertisements, where models face real-world distribution shifts characteristic of diverse advertising styles.

Our contributions, MAdVerse for structured ad analysis and SHAPES and CHARS for controlled OOD studies emphasize the importance of robust, adaptable models for both foundational research and practical applications in advertisement analysis.

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