Browse State-of-the-Art › FS-MEVQA
FS-MEVQA
7 papers with code · 1 benchmark · 1 dataset archive 2025-07-28
The Few-Shot Multimodal Explanation for Visual Question Answering (FS-MEVQA) task aims to learn MEVQA from few training samples.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| SME (7 rows) | MEAgent | Few-Shot Multimodal Explanation for Visual Question Answering | code | — | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
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.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
7 shown of 7 papers with code (7 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.
-
15 Mar 2023 11 repositories listed Syntology ran 2 of 5 samples · 3 unverified · 1 pointer-only (licence)We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
-
6 Nov 2023 4 repositories listedWe introduce CogVLM, a powerful open-source visual language foundation model.
-
Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond24 Aug 2023 2 repositories listed Syntology ran 0 of 2 samples · 2 unverified · 2 pointer-only (licence)In this work, we introduce the Qwen-VL series, a set of large-scale vision-language models (LVLMs) designed to perceive and understand both texts and images.
-
28 Oct 2024 1 repository listedFirst, we propose a new Standard Multimodal Explanation (SME) dataset and a new Few-Shot Multimodal Explanation for VQA (FS-MEVQA) task, which aims to generate the multimodal explanation of the underlying reasoning…
-
8 Mar 2024 1 repository listedIn this report, we introduce the Gemini 1.
-
1 Jan 2023 1 repository listedTo address these issues, we propose a Variational Causal Inference Network (VCIN) that establishes the causal correlation between predicted answers and explanations, and captures cross-modal relationships to generate…
-
11 Mar 2022 1 repository listed Syntology ran 2 of 2 samples · 0 unverifiedFinally, with our new data and method, we perform extensive analyses to study the effectiveness of our explanation under different settings, including multi-task learning and transfer learning.
Syntology lines on 3 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.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections