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ECOL-R: Encouraging Copying in Novel Object Captioning with Reinforcement Learning
In this paper, we focus on this challenge and propose the ECOL-R model (Encouraging Copying of Object Labels with Reinforced Learning), a copy-augmented transformer model that is encouraged to accurately describe the novel object labels.
A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems
We can then solve efficiently the problem of multi-label learning with an intractably large number of interdependent labels, such as automatic tagging of Wikipedia pages.
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