Papers › Zero-shot Visual Question Answering using Knowledge Graph
Zero-shot Visual Question Answering using Knowledge Graph
Zhuo Chen, Jiaoyan Chen, Yuxia Geng, Jeff Z. Pan, Zonggang Yuan, Huajun Chen
Incorporating external knowledge to Visual Question Answering (VQA) has become a vital practical need. Existing methods mostly adopt pipeline approaches with different components for knowledge matching and extraction, feature learning, etc.However, such pipeline approaches suffer when some component does not perform well, which leads to error propagation and poor overall performance. Furthermore, the majority of existing approaches ignore the answer bias issue -- many answers may have never appeared during training (i.e., unseen answers) in real-word application. To bridge these gaps, in this paper, we propose a Zero-shot VQA algorithm using knowledge graphs and a mask-based learning mechanism for better incorporating external knowledge, and present new answer-based Zero-shot VQA splits for the F-VQA dataset. Experiments show that our method can achieve state-of-the-art performance in Zero-shot VQA with unseen answers, meanwhile dramatically augment existing end-to-end models on the normal F-VQA task.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Visual Question Answering (VQA) | F-VQA | ZS-F-VQA | Accuracy | 88.49 | #1 of 3 | Archive leaderboard | report |
| Visual Question Answering (VQA) | F-VQA | ZS-F-VQA | MR | 9.17 | #1 of 3 | Archive leaderboard | report |
| Visual Question Answering (VQA) | F-VQA | ZS-F-VQA | MRR | 0.685 | #1 of 3 | Archive leaderboard | report |
| Visual Question Answering (VQA) | F-VQA | ZS-F-VQA | Top-1 Accuracy | 58.27 | #1 of 3 | Archive leaderboard | report |
| Visual Question Answering (VQA) | F-VQA | ZS-F-VQA | Top-3 Accuracy | 76.51 | #1 of 3 | Archive leaderboard | report |
| Visual Question Answering (VQA) | ZS-F-VQA | SAN † - hard mask | Top-1 Accuracy | 29.39 | #1 of 1 | 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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