Dysca: A Dynamic and Scalable Benchmark for Evaluating Perception Ability of LVLMs
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| Publicat a: | arXiv.org (Jul 26, 2024), p. n/a |
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| Autor principal: | |
| Altres autors: | , , , , , |
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Cornell University Library, arXiv.org
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| Accés en línia: | Citation/Abstract Full text outside of ProQuest |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3073383424 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3073383424 | ||
| 045 | 0 | |b d20240726 | |
| 100 | 1 | |a Zhang, Jie | |
| 245 | 1 | |a Dysca: A Dynamic and Scalable Benchmark for Evaluating Perception Ability of LVLMs | |
| 260 | |b Cornell University Library, arXiv.org |c Jul 26, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Currently many benchmarks have been proposed to evaluate the perception ability of the Large Vision-Language Models (LVLMs). However, most benchmarks conduct questions by selecting images from existing datasets, resulting in the potential data leakage. Besides, these benchmarks merely focus on evaluating LVLMs on the realistic style images and clean scenarios, leaving the multi-stylized images and noisy scenarios unexplored. In response to these challenges, we propose a dynamic and scalable benchmark named Dysca for evaluating LVLMs by leveraging synthesis images. Specifically, we leverage Stable Diffusion and design a rule-based method to dynamically generate novel images, questions and the corresponding answers. We consider 51 kinds of image styles and evaluate the perception capability in 20 subtasks. Moreover, we conduct evaluations under 4 scenarios (i.e., Clean, Corruption, Print Attacking and Adversarial Attacking) and 3 question types (i.e., Multi-choices, True-or-false and Free-form). Thanks to the generative paradigm, Dysca serves as a scalable benchmark for easily adding new subtasks and scenarios. A total of 8 advanced open-source LVLMs with 10 checkpoints are evaluated on Dysca, revealing the drawbacks of current LVLMs. The benchmark is released in \url{https://github.com/Benchmark-Dysca/Dysca}. | |
| 653 | |a Perception | ||
| 653 | |a Questions | ||
| 653 | |a Images | ||
| 653 | |a Free form | ||
| 653 | |a Benchmarks | ||
| 700 | 1 | |a Wang, Zhongqi | |
| 700 | 1 | |a Mengqi Lei | |
| 700 | 1 | |a Zheng, Yuan | |
| 700 | 1 | |a Yan, Bei | |
| 700 | 1 | |a Shan, Shiguang | |
| 700 | 1 | |a Chen, Xilin | |
| 773 | 0 | |t arXiv.org |g (Jul 26, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3073383424/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2406.18849 |