变压器模块网络用于在视觉问题答案中进行系统的概括
概括
神经模块网络 (NMNs) 通过使用模块化来提高变压器在视觉问题答案 (VQA) 上的性能. 变压器模块网络 (TMN) 在VQA任务的系统概括方面取得了最先进的结果.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 变压器在视觉问题答案 (VQA) 中表现强.
- 变压器在VQA中的系统概括能力,特别是与新概念组合的系统概括能力,仍然不清楚.
- 神经模块网络 (NMN) 显示出有希望的系统概括,即使是基于CNN的模块.
研究的目的:
- 调查模块化在提高 VQA 的变压器性能方面的好处.
- 引入一种新的架构,即变压器模块网络 (TMN),它将模块化集成到变压器中.
- 与现有方法相比,评估TMN的系统概括能力.
主要方法:
- 开发了变压器模块网络 (TMN),一种新的NMN架构.
- TMN使用了用于VQA的变压器模块组合.
- 在三个VQA数据集上评估TMN,专注于系统概括性能.
主要成果:
- 在三个VQA数据集上,TMN实现了最先进的系统化概括性能.
- 与标准的变压器相比,TMN在新型子任务组合上表现出超过30%的改进.
- 模块组成和模块专业化都对性能增长做出了重大贡献.
结论:
- 模块化可以显著提高VQA中的变压器的系统概括性.
- 变压器模块网络 (TMN) 为改善VQA系统提供了一个有希望的方向.
- 在TMN中模块专业化对于在新的VQA挑战中实现卓越性能至关重要.
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