11 Computer Vision News Oana Ignat Oh, yes! [she laughs] I always look for including Romania in my dataset. I really want to do more work on that area, for sure. What is the goal of this research? We want to see how foundation, state-of-the-art models, for example, CLIP – I have a recent paper that was presented at EMNLP in Singapore a few weeks ago – work across different demographics, because these models are usually made by research laboratories in Western countries and focus mostly on Western data. We want to see how well it performs across the world. In this paper, we look specifically at income. How this model works across different income levels. Images from households from different income levels in different countries. We found that there is a considerable gap in performance. The model performs much better on high-income versus low-income images. How does this help us? Well, we draw attention to this. First, we show that this gap in performance exists. This is important because it means that this model will not work well for images from those income levels or from those countries. We show that this performance is not uniform. It’s not globally uniformly distributed. We want to show that we have to make sure that we train these models on data from different countries, from different demographics, and also include
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