MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI Article Swipe
YOU?
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· 2024
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.2404.16006
Large Vision-Language Models (LVLMs) show significant strides in general-purpose multimodal applications such as visual dialogue and embodied navigation. However, existing multimodal evaluation benchmarks cover a limited number of multimodal tasks testing rudimentary capabilities, falling short in tracking LVLM development. In this study, we present MMT-Bench, a comprehensive benchmark designed to assess LVLMs across massive multimodal tasks requiring expert knowledge and deliberate visual recognition, localization, reasoning, and planning. MMT-Bench comprises $31,325$ meticulously curated multi-choice visual questions from various multimodal scenarios such as vehicle driving and embodied navigation, covering $32$ core meta-tasks and $162$ subtasks in multimodal understanding. Due to its extensive task coverage, MMT-Bench enables the evaluation of LVLMs using a task map, facilitating the discovery of in- and out-of-domain tasks. Evaluation results involving $30$ LVLMs such as the proprietary GPT-4V, GeminiProVision, and open-sourced InternVL-Chat, underscore the significant challenges posed by MMT-Bench. We anticipate that MMT-Bench will inspire the community to develop next-generation multimodal foundation models aimed at achieving general-purpose multimodal intelligence.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2404.16006
- https://arxiv.org/pdf/2404.16006
- OA Status
- green
- Cited By
- 6
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4395484118
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4395484118Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2404.16006Digital Object Identifier
- Title
-
MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGIWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-24Full publication date if available
- Authors
-
Kaining Ying, Fanqing Meng, Jin Wang, Zhiqian Li, Lin Han, Yue Yang, Hao Zhang, Wenbo Zhang, Yuqi Lin, Shuo Liu, Jiayi Lei, Quanfeng Lu, Runjian Chen, Peng Xu, Renrui Zhang, Haozhe Zhang, Peng Fei Gao, Yali Wang, Yu Qiao, Ping Luo, Kaipeng Zhang, Wenqi ShaoList of authors in order
- Landing page
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https://arxiv.org/abs/2404.16006Publisher landing page
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https://arxiv.org/pdf/2404.16006Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2404.16006Direct OA link when available
- Concepts
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Benchmark (surveying), Computer science, Artificial intelligence, Machine learning, Natural language processing, Cartography, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.tasks | 29, 55 |
| abstract_inverted_index.using | 108 |
| abstract_inverted_index.Models | 2 |
| abstract_inverted_index.across | 52 |
| abstract_inverted_index.assess | 50 |
| abstract_inverted_index.expert | 57 |
| abstract_inverted_index.models | 154 |
| abstract_inverted_index.number | 26 |
| abstract_inverted_index.study, | 41 |
| abstract_inverted_index.tasks. | 119 |
| abstract_inverted_index.visual | 13, 61, 73 |
| abstract_inverted_index.(LVLMs) | 3 |
| abstract_inverted_index.GPT-4V, | 129 |
| abstract_inverted_index.curated | 71 |
| abstract_inverted_index.develop | 150 |
| abstract_inverted_index.driving | 82 |
| abstract_inverted_index.enables | 103 |
| abstract_inverted_index.falling | 33 |
| abstract_inverted_index.inspire | 146 |
| abstract_inverted_index.limited | 25 |
| abstract_inverted_index.massive | 53 |
| abstract_inverted_index.present | 43 |
| abstract_inverted_index.results | 121 |
| abstract_inverted_index.strides | 6 |
| abstract_inverted_index.testing | 30 |
| abstract_inverted_index.various | 76 |
| abstract_inverted_index.vehicle | 81 |
| abstract_inverted_index.$31,325$ | 69 |
| abstract_inverted_index.However, | 18 |
| abstract_inverted_index.covering | 86 |
| abstract_inverted_index.designed | 48 |
| abstract_inverted_index.dialogue | 14 |
| abstract_inverted_index.embodied | 16, 84 |
| abstract_inverted_index.existing | 19 |
| abstract_inverted_index.subtasks | 92 |
| abstract_inverted_index.tracking | 36 |
| abstract_inverted_index.MMT-Bench | 67, 102, 144 |
| abstract_inverted_index.achieving | 157 |
| abstract_inverted_index.benchmark | 47 |
| abstract_inverted_index.community | 148 |
| abstract_inverted_index.comprises | 68 |
| abstract_inverted_index.coverage, | 101 |
| abstract_inverted_index.discovery | 114 |
| abstract_inverted_index.extensive | 99 |
| abstract_inverted_index.involving | 122 |
| abstract_inverted_index.knowledge | 58 |
| abstract_inverted_index.planning. | 66 |
| abstract_inverted_index.questions | 74 |
| abstract_inverted_index.requiring | 56 |
| abstract_inverted_index.scenarios | 78 |
| abstract_inverted_index.Evaluation | 120 |
| abstract_inverted_index.MMT-Bench, | 44 |
| abstract_inverted_index.MMT-Bench. | 140 |
| abstract_inverted_index.anticipate | 142 |
| abstract_inverted_index.benchmarks | 22 |
| abstract_inverted_index.challenges | 137 |
| abstract_inverted_index.deliberate | 60 |
| abstract_inverted_index.evaluation | 21, 105 |
| abstract_inverted_index.foundation | 153 |
| abstract_inverted_index.meta-tasks | 89 |
| abstract_inverted_index.multimodal | 9, 20, 28, 54, 77, 94, 152, 159 |
| abstract_inverted_index.reasoning, | 64 |
| abstract_inverted_index.underscore | 134 |
| abstract_inverted_index.navigation, | 85 |
| abstract_inverted_index.navigation. | 17 |
| abstract_inverted_index.proprietary | 128 |
| abstract_inverted_index.rudimentary | 31 |
| abstract_inverted_index.significant | 5, 136 |
| abstract_inverted_index.applications | 10 |
| abstract_inverted_index.development. | 38 |
| abstract_inverted_index.facilitating | 112 |
| abstract_inverted_index.meticulously | 70 |
| abstract_inverted_index.multi-choice | 72 |
| abstract_inverted_index.open-sourced | 132 |
| abstract_inverted_index.recognition, | 62 |
| abstract_inverted_index.capabilities, | 32 |
| abstract_inverted_index.comprehensive | 46 |
| abstract_inverted_index.intelligence. | 160 |
| abstract_inverted_index.localization, | 63 |
| abstract_inverted_index.out-of-domain | 118 |
| abstract_inverted_index.InternVL-Chat, | 133 |
| abstract_inverted_index.understanding. | 95 |
| abstract_inverted_index.Vision-Language | 1 |
| abstract_inverted_index.general-purpose | 8, 158 |
| abstract_inverted_index.next-generation | 151 |
| abstract_inverted_index.GeminiProVision, | 130 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 22 |
| citation_normalized_percentile |