Learning Distinct and Representative Modes for Image Captioning. (arXiv:2209.08231v1 [cs.CV])

Over the years, state-of-the-art (SoTA) image captioning methods have
achieved promising results on some evaluation metrics (e.g., CIDEr). However,
recent findings show that the captions generated by these methods tend to be
biased toward the "average" caption that only captures the most general mode
(a.k.a, language pattern) in the training corpus, i.e., the so-called mode
collapse problem. Affected by it, the generated captions are limited in
diversity and usually less informative than natural image descriptions made by
humans. In this …

arxiv captioning image