Papers › Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
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
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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Code
Syntology Ran 9 of 9 code samples harvested from 3 repositories linked to this paper; 0 have no recorded run. Of those that ran: 1 ran · violated contract; 8 ran · our draft was wrong.
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Code Syntology ran Syntology
9 samples harvested; 9 ran; 0 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Visual Question Answering (VQA) | GQA Test2019 | BottomUp | Accuracy | 49.74 | #107 of 127 | Archive leaderboard | report |
| Visual Question Answering (VQA) | GQA Test2019 | BottomUp | Binary | 66.64 | #107 of 127 | Archive leaderboard | report |
| Visual Question Answering (VQA) | GQA Test2019 | BottomUp | Consistency | 78.71 | #107 of 127 | Archive leaderboard | report |
| Visual Question Answering (VQA) | GQA Test2019 | BottomUp | Distribution | 5.98 | #107 of 127 | Archive leaderboard | report |
| Visual Question Answering (VQA) | GQA Test2019 | BottomUp | Open | 34.83 | #107 of 127 | Archive leaderboard | report |
| Visual Question Answering (VQA) | GQA Test2019 | BottomUp | Plausibility | 84.57 | #107 of 127 | Archive leaderboard | report |
| Visual Question Answering (VQA) | GQA Test2019 | BottomUp | Validity | 96.18 | #107 of 127 | Archive leaderboard | report |
| Visual Question Answering (VQA) | VQA v2 test-std | Up-Down | overall | 70.34 | #28 of 38 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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