Guiding Image Captioning Models Toward More Specific Captions
Simon Kornblith Lala Li Zirui Wang Thao Nguyen
Image captioning is conventionally formulated as the task of generating
captions for images that match the distribution of reference image-caption
pairs. However, reference captions in standard captioning datasets are short
and may not uniquely identify the images they describe. These problems are
further exacerbated when models are trained directly on image-alt text pairs
collected from the internet. In this work, we show that it is possible to
generate more specific captions with minimal changes to the training process.
We implement classifier-free guidance for an autoregressive captioning model by
fine-tuning it to estimate both conditional and unconditional distributions
over captions. The guidance scale applied at decoding controls a trade-off
between maximizing $p(\mathrm{caption}|\mathrm{image})$ and
$p(\mathrm{image}|\mathrm{caption})$. Compared to standard greedy decoding,
decoding with a guidance scale of 2 substantially improves reference-free
metrics such as CLIPScore (0.808 vs. 0.775) and caption$\to$image retrieval
performance in the CLIP embedding space (recall@1 44.6% vs. 26.5%), but worsens
standard reference-based captioning metrics (e.g., CIDEr 78.6 vs 126.1). We
further explore the use of language models to guide the decoding process,
obtaining small improvements over the Pareto frontier of reference-free vs.
reference-based captioning metrics that arises from classifier-free guidance,
and substantially improving the quality of captions generated from a model
trained only on minimally curated web data.