![]() Simulated experiments on a 125k image subset of the ImageNet100 show that it can be annotated to 80% top-1 accuracy with 0.35 annotations per image on average, a 2.7x and 6.7x improvement over prior work and manual annotation, respectively. Our analysis is done in a more realistic simulation that involves querying human labelers, which uncovers issues with evaluation using existing worker simulation methods. Specifically, we make use of advances in self-supervised learning, view annotation as a semi-supervised learning problem, identify and mitigate pitfalls and ablate several key design choices to propose effective guidelines for labeling. Building on prior work on online joint probabilistic modeling of human annotations and machine-generated beliefs, we propose modifications and best practices aimed at minimizing human labeling effort. While methods that exploit learnt models for labeling exist, a surprisingly prevalent approach is to query humans for a fixed number of labels per datum and aggregate them, which is expensive. In this paper, we investigate efficient annotation strategies for collecting multi-class classification labels for a large collection of images. This is expensive, and guaranteeing the quality of the labels is a major challenge. You can use arrows to point to quotes that you know you will use during the essay.Download a PDF of the paper titled Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets, by Yuan-Hong Liao and 2 other authors Download PDF Abstract:Data is the engine of modern computer vision, which necessitates collecting large-scale datasets. For example, an asterisk in the margins or top of the page could identify pages with major parts of the argument.
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