The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Open accessAbstract
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.
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Cites
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Microsoft COCO: Common Objects in Context
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ImageNet Large Scale Visual Recognition Challenge
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Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., et al. (2018) · arXiv · doi:10.48550/arxiv.1804.03999 · Cited by 5,458
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
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“Natural language processing (NLP): There are few NLPbased subjects such as neural machine translation [508] and some other interdisciplinary subjects such as image captioning [509], [510], [511], [512], visual question answering (VQA) [513], [514], [515], [516], [517] which are closely associated with NLP.”
BackgroundLXMERT: Learning Cross-Modality Encoder Representations from Transformers
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ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
Lu, J., Batra, D., Parikh, D., & Lee, S. (2019) · arXiv · doi:10.48550/arxiv.1908.02265 · Cited by 2,109
Grounding DINO: Marrying DINO with Grounded Pre-training for Open-Set Object Detection
Liu, S., Zeng, Z., Ren, T., Li, F., Zhang, H., Yang, J., Jiang, Q., Li, C., Yang, J., Su, H., et al. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-72970-6_3 · Cited by 2,074
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Metadata
- Authors
- Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., & Zhang, L.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 6077-6086
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2745461083
- Cited by
- 4,948
- References
- 53
- FWCI
- 159.08
- Type
- conference paper
- Language
- en
- Open access
- green
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