Reproduce “Transform and Tell: Entity-Aware News Image Captioning” on OpenResearch
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About this paper
Alasdair Tran, Alexander Mathews, Lexing Xie
The "Transform and Tell" model generates entity-aware news image captions by integrating multi-modal information from images, faces, objects, and accompanying articles using a Transformer architecture. It achieves a four-fold increase in CIDEr score on the GoodNews dataset and successfully generates unseen named entities and linguistically richer descriptions.
Key insights
- Simultaneously attending to image patches, faces, objects, and article text through multi-modal attention is crucial for accurate entity grounding and contextual understanding in news image captioning.
- Utilizing Byte-Pair Encoding (BPE) with powerful pre-trained language models like RoBERTa enables an open-vocabulary approach, which is essential for generating rare and unseen named entities.
- Incorporating the image's location within an article as a contextual cue significantly enhances performance, and Transformer decoders demonstrate superior capacity over LSTMs for complex language generation.
Summary by alphaXiv.
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