Browse State-of-the-Art › VCGBench-Diverse
VCGBench-Diverse
5 papers with code · 1 benchmark · 1 dataset archive 2025-07-28
Recognizing the limited diversity in existing video conversation benchmarks, we introduce VCGBench-Diverse to comprehensively evaluate the generalization ability of video LMMs. While VCG-Bench provides an extensive evaluation protocol, it is limited to videos from the ActivityNet200 dataset. Our benchmark comprises a total of 877 videos, 18 broad video categories and 4,354 QA pairs, ensuring a robust evaluation framework.
The evaluation is computed over five different aspects:
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Correctness of information
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Detail orientation
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Contextual understanding
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Temporal understanding
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Consistency.
Additionally, VCGBench-Diverse provides a breakdown of performance across three key aspects:
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Dense video captioning, which assesses the ability to generate detailed and accurate descriptions of the video content,
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Spatial understanding, which evaluates the capability to understand and describe the spatial relationships and settings within the video
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Reasoning, which tests the adeptness in inferring and explaining causal relationships and actions within the video.
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 |
|---|---|---|---|---|---|
| VideoInstruct (6 rows) | VideoGPT+ | VideoGPT+: Integrating Image and Video Encoders for Enhanced Video... | code | Syntology ran 6 of 8 samples · 2 unverified | 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.
Most implemented papers archive 2025-07-28
5 shown of 5 papers with code (5 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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14 Nov 2023 4 repositories listedLarge language models have demonstrated impressive universal capabilities across a wide range of open-ended tasks and have extended their utility to encompass multimodal conversations.
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28 Nov 2023 3 repositories listed Syntology ran 7 of 10 samples · 3 unverifiedWith the rapid development of Multi-modal Large Language Models (MLLMs), a number of diagnostic benchmarks have recently emerged to evaluate the comprehension capabilities of these models.
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8 Jun 2023 2 repositories listedConversation agents fueled by Large Language Models (LLMs) are providing a new way to interact with visual data.
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13 Jun 2024 1 repository listed Syntology ran 6 of 8 samples · 2 unverified · 8 pointer-only (licence)Building on the advances of language models, Large Multimodal Models (LMMs) have contributed significant improvements in video understanding.
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30 Nov 2023 1 repository listed Syntology ran 5 of 11 samples · 6 unverified · 11 pointer-only (licence)Large language models (LLMs) have shown remarkable text understanding capabilities, which have been extended as Video LLMs to handle video data for comprehending visual details.
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.
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