Browse State-of-the-Art › Audio-Visual Question Answering (AVQA)
Audio-Visual Question Answering (AVQA)
15 papers with code · 0 benchmarks · 1 dataset archive 2025-07-28
Benchmarks archive 2025-07-28
No benchmark for this task in the archive.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
1 dataset whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Most implemented papers archive 2025-07-28
15 shown of 15 papers with code (20 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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18 May 2023 2 repositories listed Syntology ran 2 of 7 samples · 5 unverifiedIn this work, we explore a scalable way for building a general representation model toward unlimited modalities.
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1 Apr 2025 1 repository listedThe first stage expands the test space with greater diversity, while the second enables a refined robustness evaluation across rare, frequent, and overall question distributions.
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6 Mar 2025 1 repository listedHowever, existing methods mainly use question information implicitly, limiting focus on question-specific details.
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30 Jul 2024 1 repository listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)The Audio Visual Question Answering (AVQA) task aims to answer questions related to various visual objects, sounds, and their interactions in videos.
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23 Jul 2024 1 repository listed Syntology ran 20 of 24 samples · 4 unverified · 3 pointer-only (licence)Recent Audio-Visual Question Answering (AVQA) methods rely on complete visual and audio input to answer questions accurately.
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13 Jun 2024 1 repository listedIn this paper, we work towards extending Audio-Visual Question Answering (AVQA) to multilingual settings.
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18 Apr 2024 1 repository listed Syntology ran 2 of 3 samples · 1 unverified · 1 pointer-only (licence)The former leads to a large, diverse test space, while the latter results in a comprehensive robustness evaluation on rare, frequent, and overall questions.
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11 Mar 2024 1 repository listedAudio-visual question answering (AVQA) requires reference to video content and auditory information, followed by correlating the question to predict the most precise answer.
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CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual Scenarios7 Mar 2024 1 repository listed Syntology ran 6 of 12 samples · 6 unverifiedThis paper focuses on the challenge of answering questions in scenarios that are composed of rich and complex dynamic audio-visual components.
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20 Dec 2023 1 repository listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)These selected pairs are constrained to have larger similarity values than the mismatched pairs.
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10 Aug 2023 1 repository listedSuch naturally multi-modal videos are composed of rich and complex dynamic audio-visual components, where most of which could be unrelated to the given questions, or even play as interference in answering the content of…
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21 May 2023 1 repository listedRecent works rely on elaborate target-agnostic parsing of audio-visual scenes for spatial grounding while mistreating audio and video as separate entities for temporal grounding.
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17 Apr 2023 1 repository listedDifferent from widely-studied vision-language pretraining models, VALOR jointly models relationships of vision, audio and language in an end-to-end manner.
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26 Mar 2022 1 repository listed Syntology ran 0 of 4 samples · 4 unverifiedIn this paper, we focus on the Audio-Visual Question Answering (AVQA) task, which aims to answer questions regarding different visual objects, sounds, and their associations in videos.
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25 Feb 2020 1 repository listedVideo question answering (VideoQA) is challenging as it requires modeling capacity to distill dynamic visual artifacts and distant relations and to associate them with linguistic concepts.
Syntology lines on 7 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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