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

11 Apr 2017arXiv:1704.03162archive 2025-07-28

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.

Cyanogenoid/pytorch-vqa mentioned on GitHubpytorch report
Gunnika/Visual-Question-Answering mentioned on GitHubpytorch report
abhigoyal1997/CS-763-Project mentioned on GitHubpytorch report
deshanadesai/VQA-DataAugmentation mentioned on GitHubpytorch report
dukelin95/vqa_pytorch mentioned on GitHubpytorch report
guoyang9/vqa-prior mentioned on GitHubpytorch report
mkhalil1998/EC601_Group_Project mentioned on GitHubpytorch report
myaoo18/EC601-Visual-Question-Answering mentioned on GitHubpytorch report
pramodkaushik/visual_qa_analysis mentioned on GitHubpytorch report
snagiri/ECE285_Jarvis_ProjectA mentioned on GitHubpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

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.

1ran · honoured contract
1ran · violated contract
5ran · our draft was wrong
2ran · fixture could not drive it

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apply_attention Cyanogenoid/pytorch-vqa/model.py community (archive-listed) ran · honoured contract fingerprinted no licence file found · pointer only · 6e1df46e2ff7c334 · report
extract_vocab guoyang9/vqa-prior/preprocess/preprocess-vocab.py community (archive-listed) ran · our draft was wrong licence not identified · pointer only · 4b6eb764bb004b1f · report
sort_batch abhigoyal1997/CS-763-Project/src/vqa_model.py community (archive-listed) ran · our draft was wrong fingerprinted no licence file found · pointer only · 1877d63c9e9a8d2e · report
tile_2d_over_nd Cyanogenoid/pytorch-vqa/model.py community (archive-listed) ran · violated contract fingerprinted no licence file found · pointer only · aaac9d55feeb4a0e · report
apply_attention identical code first harvested elsewhere ran · our draft was wrong fingerprinted licence of this copy not recorded · 388d2a6ba37219cd · report
create_submission identical code first harvested elsewhere ran · fixture could not drive it licence of this copy not recorded · d12f1716d21540a8 · report
predict_answers identical code first harvested elsewhere ran · fixture could not drive it licence of this copy not recorded · 6d3a7a351f38eccd · report
repeat_encoded_question identical code first harvested elsewhere ran · our draft was wrong fingerprinted licence of this copy not recorded · fe3010862617673c · report
to_var identical code first harvested elsewhere ran · our draft was wrong fingerprinted licence of this copy not recorded · 502c50812b7f31ab · report

Tasks

Visual Question AnsweringVisual Question Answering (VQA)

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

1x1 ConvolutionAverage PoolingBatch NormalizationBottleneck Residual BlockConvolutionGlobal Average PoolingKaiming InitializationMax PoolingReLUResidual BlockResidual Connection

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