Clue: Cross-modal coherence modeling for caption generation

Malihe Alikhani, Piyush Sharma, shengjie li, Radu Soricut, Matthew Stone

Abstract:

We use coherence relations inspired by computational models of discourse to study the information needs and goals of image captioning.
Using an annotation protocol specifically devised for capturing image–caption coherence
relations, we annotate 10,000 instances from
publicly-available image–caption pairs. We introduce a new task for learning inferences in
imagery and text, coherence relation prediction, and show that these coherence annotations can be exploited to learn relation classifiers as an intermediary step, and also train
coherence-aware, controllable image captioning models. The results show a dramatic improvement in the consistency and quality of
the generated captions with respect to information needs specified via coherence relations