Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Models Article Swipe
Chen Ju
,
Haicheng Wang
,
Zeqian Li
,
Xu Chen
,
Zhonghua Zhai
,
Weilin Huang
,
Shuai Xiao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.07408
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.07408
Vision-Language Large Models (VLMs) have become primary backbone of AI, due to the impressive performance. However, their expensive computation costs, i.e., throughput and delay, impede potentials in real-world scenarios. To achieve acceleration for VLMs, most existing methods focus on the model perspective: pruning, distillation, quantification, but completely overlook the data-perspective redundancy. To fill the overlook, this paper pioneers the severity of data redundancy, and designs one plug-and-play Turbo module guided by information degree to prune inefficient tokens from visual or textual data. In pursuit of efficiency-performance trade-offs, information degree takes two key factors into consideration: mutual redundancy and semantic value. Concretely, the former evaluates the data duplication between sequential tokens; while the latter evaluates each token by its contribution to the overall semantics. As a result, tokens with high information degree carry less redundancy and stronger semantics. For VLMs' calculation, Turbo works as a user-friendly plug-in that sorts data referring to information degree, utilizing only top-level ones to save costs. Its advantages are multifaceted, e.g., being generally compatible to various VLMs across understanding and generation, simple use without retraining and trivial engineering efforts. On multiple public VLMs benchmarks, we conduct extensive experiments to reveal the gratifying acceleration of Turbo, under negligible performance drop.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.07408
- https://arxiv.org/pdf/2312.07408
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389712734
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389712734Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2312.07408Digital Object Identifier
- Title
-
Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language ModelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-12Full publication date if available
- Authors
-
Chen Ju, Haicheng Wang, Zeqian Li, Xu Chen, Zhonghua Zhai, Weilin Huang, Shuai XiaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2312.07408Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2312.07408Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2312.07408Direct OA link when available
- Concepts
-
Computer science, Redundancy (engineering), Turbo, Language model, Inference, Artificial intelligence, Data mining, Engineering, Automotive engineering, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.scenarios. | 28 |
| abstract_inverted_index.semantics. | 122, 136 |
| abstract_inverted_index.sequential | 108 |
| abstract_inverted_index.throughput | 21 |
| abstract_inverted_index.Concretely, | 100 |
| abstract_inverted_index.benchmarks, | 187 |
| abstract_inverted_index.computation | 18 |
| abstract_inverted_index.duplication | 106 |
| abstract_inverted_index.engineering | 181 |
| abstract_inverted_index.experiments | 191 |
| abstract_inverted_index.generation, | 174 |
| abstract_inverted_index.inefficient | 75 |
| abstract_inverted_index.information | 71, 87, 129, 151 |
| abstract_inverted_index.performance | 201 |
| abstract_inverted_index.redundancy, | 62 |
| abstract_inverted_index.redundancy. | 50 |
| abstract_inverted_index.trade-offs, | 86 |
| abstract_inverted_index.acceleration | 31, 196 |
| abstract_inverted_index.calculation, | 139 |
| abstract_inverted_index.contribution | 118 |
| abstract_inverted_index.performance. | 14 |
| abstract_inverted_index.perspective: | 41 |
| abstract_inverted_index.distillation, | 43 |
| abstract_inverted_index.multifaceted, | 163 |
| abstract_inverted_index.plug-and-play | 66 |
| abstract_inverted_index.understanding | 172 |
| abstract_inverted_index.user-friendly | 144 |
| abstract_inverted_index.consideration: | 94 |
| abstract_inverted_index.Vision-Language | 0 |
| abstract_inverted_index.quantification, | 44 |
| abstract_inverted_index.data-perspective | 49 |
| abstract_inverted_index.efficiency-performance | 85 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |