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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Advancing Motion with LLMs: Leveraging Large Language Models for Enhanced Text-Conditioned Motion Generation and Retrieval

Kalakonda Sai Shashank

Abstract

In the field of artificial intelligence, the generation of human-like motion from natural language descriptions has garnered increasing attention across various research domains. Computer vision focuses on understanding and replicating visual cues for motion, while computer graphics aims to create and edit visually realistic animations. Similarly, multimedia research explores the intersection of data modalities, such as text, motion, and image, to enhance user experiences. Robotics and human-computer interaction are pivotal areas where language-driven motion systems improve the autonomy and responsiveness of machines, facilitating more efficient and meaningful human-robot interactions. Despite its significance, existing approaches still encounter significant difficulties, particularly when generating motions from unseen or novel text descriptions. These models often lack the ability to fully capture intricate, low-level motion nuances that go beyond basic action labels. This limitation arises from the reliance on brief and simplistic textual descriptions, which fail to convey the complex and fine-grained characteristics of human motion, resulting in less diverse and realistic outputs. As a result, the generated motions frequently lack the subtlety and depth required for more dynamic and context-specific applications.

This thesis introduces two key contributions to overcome these limitations and advance text-conditioned human motion generation. First, we present Action-GPT, a novel framework aimed at significantly enhancing text-based action generation models by incorporating Large Language Models (LLMs). Traditional motion capture datasets tend to provide action descriptions that are brief and minimalistic, often failing to convey the full range of complexities involved in human movement. Such sparse descriptions limit the ability of models to generate diverse and nuanced motion sequences. Action-GPT leverages LLMs to create richer, more detailed descriptions of actions, capturing finer aspects of movement. By doing so, it improves the alignment between text and motion spaces, enabling models to generate more precise and contextually accurate motion sequences. This framework is designed to work with both stochastic models (e.g., VAE-based) and deterministic models offering flexibility across different types of motion generation architectures. Experimental results demonstrate that Action-GPT not only enhances the quality of synthesized motions—both in terms of realism and diversity—but also excels in zero-shot generation, effectively handling previously unseen text descriptions.

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Towards Understanding Small Objects in Indian Driving Situations

Umamahesh Janapareddi

Abstract

In Indian urban and rural driving scenarios, small objects are pervasive and often crucial for safe navigation. These objects can include pedestrians crossing roads, children playing near streets, cyclists, stray animals, as well as small vehicles like scooters and motorbikes. Additionally, traffic signs, signal lights, potholes, and road markings (such as lane dividers or zebra crossings) are often small in size but essential for driving decisions. In such contexts, missing or inaccurately segmenting these small objects can lead to critical errors in detection, causing accidents or delays in the vehicle’s decision-making process. Automated understanding of such objects need detection and segmentation to start with.

Semantic Segmentation is a critical task in computer vision with a wide range of applications. The objective is to partition an image—a collection of pixels—into distinct labeled regions, each corresponding to specific objects or parts of the scene. This process is crucial for scene understanding and enables the localization of objects within the image. Over time, significant progress has been made in semantic segmentation, especially with the advent of deep learning. The advances in this area have revolutionized computer vision, pushing beyond traditional methods and achieving remarkable improvements in performance.

When discussing semantic segmentation, we often focus on datasets, the objects within those datasets, and their corresponding segmentations. While many datasets exist for road scenarios, particularly those representing Western road conditions, there is relatively little research on road conditions specific to India. One notable exception is the Indian Driving Dataset (IDD), a dataset specifically designed for semantic segmentation of Indian road scenarios.

Road and driving datasets typically contain objects of varying sizes within each class label. These objects can be broadly categorized into three types: small, medium, and large. The importance of segmentation is well understood across several domains such as medical imaging, autonomous vehicles, aerial imagery, robotics, surveillance, and industrial automation. However, one of the most challenging problems in segmentation is the segmentation of small objects. Small object segmentation is particularly difficult due to factors such as (i) the limited number of pixels representing small objects, (ii) class imbalance during training, and (iii) the inherent challenges posed by small object representations. These factors hinder the performance of deep learning architectures, making it harder for modern techniques to accurately handle small objects.

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Multi-Object Multi-Part Scene Parsing

Pranav Gupta

Abstract

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