arXiv:2511.04247v1 Announce Type: new
Abstract: Multimodal co-embedding models, especially CLIP, have advanced the state of the art in zero-shot classification and multimedia information retrieval in recent years by aligning images and text in a shared representation space. However, such modals trained on a contrastive alignment can lack stability towards small input perturbations. Especially when dealing with manually expressed queries, minor variations in the query can cause large differences in the ranking of the best-matching results. In this paper, we present a systematic analysis of the effect of multiple classes of non-semantic query perturbations in an multimedia information retrieval scenario. We evaluate a diverse set of lexical, syntactic, and semantic perturbations across multiple CLIP variants using the TRECVID Ad-Hoc Video Search queries and the V3C1 video collection. Across models, we find that syntactic and semantic perturbations drive the largest instabilities, while brittleness is concentrated in trivial surface edits such as punctuation and case. Our results highlight robustness as a critical dimension for evaluating vision-language models beyond benchmark accuracy.
Expert Commentary: The Multidisciplinary Nature of Multimedia Information Systems
Understanding the multi-disciplinary nature of multimedia information systems is crucial in advancing the field of computer vision and natural language processing. The concept of multimodal co-embedding models, such as CLIP, highlights the importance of aligning images and text in a shared representation space for tasks like zero-shot classification and multimedia information retrieval. By leveraging both visual and textual information, these models have shown promising results in various applications.
Relationship to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
Animations, Artificial Reality, Augmented Reality, and Virtual Realities are all closely related to the concepts discussed in the article. The alignment of images and text in a shared representation space, as seen in CLIP and other multimodal models, can contribute to the development of more immersive and interactive experiences in these domains. By understanding the effect of non-semantic query perturbations on multimedia information retrieval, researchers can improve the robustness and reliability of vision-language models in various applications, including virtual and augmented reality environments.
Analysis and Insights
The systematic analysis presented in this paper sheds light on the impact of different types of query perturbations on the performance of vision-language models. By evaluating lexical, syntactic, and semantic variations in manually expressed queries, researchers can identify the factors that contribute to instability and brittleness in these models. This analysis highlights the importance of robustness in evaluating vision-language models beyond benchmark accuracy, emphasizing the need for models that can handle small input perturbations while maintaining consistent performance.
Future Directions
Building on this research, future studies could focus on developing more robust and stable vision-language models that can handle a wide range of query perturbations. By enhancing the resilience of these models to syntactic and semantic variations, researchers can improve their performance in real-world multimedia information retrieval scenarios. Additionally, exploring the connection between multimodal co-embedding models and virtual/augmented reality applications could lead to exciting advancements in interactive storytelling, immersive gaming experiences, and other multimedia content creation.