Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Peter Anderson,Xiaodong He,Chris Buehler,Damien Teney,Mark Johnson,Stephen Gould,Lei Zhang +6 more
- pp 6077-6086
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TLDR
In this paper, a bottom-up and top-down attention mechanism was proposed to enable attention to be calculated at the level of objects and other salient image regions, which achieved state-of-the-art results on the MSCOCO test server.Abstract:
Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. In this work, we propose a combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions. This is the natural basis for attention to be considered. Within our approach, the bottom-up mechanism (based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state-of-the-art for the task, achieving CIDEr / SPICE / BLEU-4 scores of 117.9, 21.5 and 36.9, respectively. Demonstrating the broad applicability of the method, applying the same approach to VQA we obtain first place in the 2017 VQA Challenge.read more
Citations
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Fast, Diverse and Accurate Image Captioning Guided by Part-Of-Speech
TL;DR: In this paper, the authors use part-of-speech as summaries, since their summary should drive caption generation and achieve high accuracy for the diverse captions as evaluated by standard captioning metrics and user studies.
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Iterative Answer Prediction with Pointer-Augmented Multimodal Transformers for TextVQA
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