Deep learning, the most important subfield of machine learning and artificial intelligence (AI) over the last decade, is considered one of the fundamental technologies underpinning the Fourth Industrial Revolution. But despite its record-breaking history, deep learning’s enormous appetite for compute and data means that sometimes it can be too costly to practically use. In this paper, we connect technical insights from deep learning scaling laws and transfer learning with the economics of IT to propose a framework for estimating the cost of deep learning computer vision systems to achieve a desired level of accuracy. Our tool can be of practical use to AI practitioners in industry or academia to guide investment decisions.

Captions that describe or explain charts help improve recall and comprehension of the depicted data and provide a more accessible medium for people with visual disabilities.However, current approaches for automatically generating such captions struggle to articulate the perceptual or cognitive features that are the hallmark of charts (e.g., complex trends and patterns).In response, we introduce VisText: a dataset of 12,441 pairs of charts and captions that describe the charts' construction, report key statistics, and identify perceptual and cognitive phenomena.In VisText, a chart is available as three representations: a rasterized image, a backing data table, and a scene graph -a hierarchical representation of a chart's visual elements akin to a web page's Document Object Model (DOM).To evaluate the impact of VisText, we fine-tune state-of-the-art language models on our chart captioning task and apply prefix-tuning to produce captions that vary the semantic content they convey.Our models generate coherent, semantically rich captions and perform on par with state-of-the-art chart captioning models across machine translation and text generation metrics.Through qualitative analysis, we identify six broad categories of errors that our models make that can inform future work. Rasterized ImageData Table Scene Graph {title: "