375,137 research outputs found
Generating Diverse and Meaningful Captions: Unsupervised Specificity Optimization for Image Captioning
Image Captioning is a task that requires models to acquire a multi-modal understanding of the world and to express this understanding in natural language text. While the state-of-the-art for this task has rapidly improved in terms of n-gram metrics, these models tend to output the same generic captions for similar images. In this work, we address this limitation and train a model that generates more diverse and specific captions through an unsupervised training approach that incorporates a learning signal from an Image Retrieval model. We summarize previous results and improve the state-of-the-art on caption diversity and novelty.
We make our source code publicly available online: https://github.com/AnnikaLindh/Diverse_and_Specific_Image_Captionin
Generating Diverse and Meaningful Captions
Image Captioning is a task that requires models to acquire a multi-modal
understanding of the world and to express this understanding in natural
language text. While the state-of-the-art for this task has rapidly improved in
terms of n-gram metrics, these models tend to output the same generic captions
for similar images. In this work, we address this limitation and train a model
that generates more diverse and specific captions through an unsupervised
training approach that incorporates a learning signal from an Image Retrieval
model. We summarize previous results and improve the state-of-the-art on
caption diversity and novelty. We make our source code publicly available
online.Comment: Accepted for presentation at The 27th International Conference on
Artificial Neural Networks (ICANN 2018
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