3 accepted papers
From content moderation to wildlife conservation the number of applications that require models to recognize nuanced or subjective visual concepts is growing. Traditionally developing classifiers for such concepts requires substantial manual effort measured in hours days or even months to identify a…
Pre-trained representations are becoming crucial for many NLP and perception tasks. While representation learning in NLP has transitioned to training on raw text without human annotations, visual and vision-language representations still rely heavily on curated training datasets that are expensive o…
Most deep architectures for image classification--even those that are trained to classify a large number of diverse categories--learn shared image representations with a single model. Intuitively, however, categories that are more similar should share more information than those that are very differ…