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Incorporating Geo-Diverse Knowledge into Prompting for Increased Geographical Robustness in Object Recognition
In the absence of training data from target geographies, we hypothesize that geographically diverse descriptive knowledge of categories can enhance robustness.
Investigating the Role of Attribute Context in Vision-Language Models for Object Recognition and Detection
Methods are mostly evaluated in terms of how well object class names are learned, but captions also contain rich attribute context that should be considered when learning object alignment.
Contrastive View Design Strategies to Enhance Robustness to Domain Shifts in Downstream Object Detection
To address this gap, we conduct an empirical study of contrastive learning and out-of-domain object detection, studying how contrastive view design affects robustness.
Story Understanding in Video Advertisements
We dedicate our study to understand the dynamic structure of video ads automatically.
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