Papers › Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering
Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering
Vahid Kazemi, Ali Elqursh
This paper presents a new baseline for visual question answering task. Given an image and a question in natural language, our model produces accurate answers according to the content of the image. Our model, while being architecturally simple and relatively small in terms of trainable parameters, sets a new state of the art on both unbalanced and balanced VQA benchmark. On VQA 1.0 open ended challenge, our model achieves 64.6% accuracy on the test-standard set without using additional data, an improvement of 0.4% over state of the art, and on newly released VQA 2.0, our model scores 59.7% on validation set outperforming best previously reported results by 0.5%. The results presented in this paper are especially interesting because very similar models have been tried before but significantly lower performance were reported. In light of the new results we hope to see more meaningful research on visual question answering in the future.
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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 · honoured contract; 1 ran · violated contract; 5 ran · our draft was wrong; 2 ran · fixture could not drive it.
By repository: community (archive-listed): 4 samples from 3 repositories, 4 ran; 5 identical to code first harvested elsewhere. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
13 repositories listed; official and paper-mentioned ones first.
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Code Syntology ran Syntology
9 samples harvested; 9 ran; 1 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.
Licence: 9 of the 9 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from 3 repositories linked to this paper, official or community; each sample names its own and says which. Some samples are identical code Syntology first harvested from another repository; for those, this paper's copy is not located and its licence is not recorded. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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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) | VQA v1 test-dev | SAAA (ResNet) | Accuracy | 64.5 | #1 of 7 | Archive leaderboard | report |
| Visual Question Answering (VQA) | VQA v1 test-std | SAAA (ResNet) | Accuracy | 64.6 | #1 of 6 | 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.
Methods
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