Attend to You: Personalized Image Captioning
with Context Sequence Memory Networks
Abstract
We address personalization issues of image captioning,
which have not been discussed yet in previous research. For
a query image, we aim to generate a descriptive sentence,
accounting for prior knowledge such as the user’s active vocabularies in previous documents. As applications of personalized image captioning, we tackle two post automation
tasks: hashtag prediction and post generation, on our newly
collected Instagram dataset, consisting of 1.1M posts from
6.3K users. We propose a novel captioning model named
Context Sequence Memory Network (CSMN). Its unique updates over previous memory network models include (i) exploiting memory as a repository for multiple types of context
information, (ii) appending previously generated words into
memory to capture long-term information without suffering from the vanishing gradient problem, and (iii) adopting
CNN memory structure to jointly represent nearby ordered
memory slots for better context understanding. With quantitative evaluation and user studies via Amazon Mechanical
Turk, we show the effectiveness of the three novel features of
CSMN and its performance enhancement for personalized
image captioning over state-of-the-art captioning models.