利用累积学习策略克服视觉问题解答中的语言先验

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

摘要

视觉问题解答(VQA)的性能在过去几年中取得了长足进步。然而,目前的许多视觉问题回答模型往往依赖于问题与答案之间肤浅的语言相关性,往往无法从视觉和语言两方面充分学习多模态知识,从而导致性能大幅下降。为了解决这个问题,我们开发了 VQA-CP v2.0 数据集,通过对 VQA v2.0 的训练集和测试集的分布进行贪婪的重新划分来减少语言偏差。要在真实世界的数据集上实现高性能,就必须有效地学习小类,因此我们在本文中分析了 VQA-CP v2.0 数据集中存在的倾斜长尾分布,并提出了一种新的基于集合的参数敏感框架。该框架建立在两个表征学习分支和一个联合学习区块之上,旨在减少 VQA 任务中的语言偏差。具体来说,表征学习分支可以确保从主要和次要类别中学习到的卓越代表能力。联合学习模块迫使模型最初集中于主要类别以获得稳健的表征,然后在训练过程中逐渐将注意力转移到次要类别的分类上。实验结果表明,在 VQA-CP v2.0 数据集上,我们的方法无需额外注释即可超越最先进的方法。值得注意的是,在 "num "类型上,我们的框架比第二好的方法(无额外注释)高出 8.64%。同时,与基线模型相比,我们的方法在 VQA v2.0 数据集上并没有牺牲准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Overcoming language priors in visual question answering with cumulative learning strategy

The performance of visual question answering(VQA) has witnessed great progress over the last few years. However, many current VQA models tend to rely on superficial linguistic correlations between questions and answers, often failing to sufficiently learn multi-modal knowledge from both vision and language, and thus suffering significant performance drops. To address this issue, the VQA-CP v2.0 dataset was developed to reduce language biases by greedily re-partitioning the distribution of VQA v2.0’s training and test sets. According to the fact that achieving high performance on real-world datasets requires effective learning from minor classes, in this paper we analyze the presence of skewed long-tail distributions in the VQA-CP v2.0 dataset and propose a new ensemble-based parameter-insensitive framework. This framework is built on two representation learning branches and a joint learning block, which are designed to reduce language biases in VQA tasks. Specifically, the representation learning branches can ensure the superior representative ability learned from the major and minor classes. The joint learning block forces the model to initially concentrate on major classes for robust representation and then gradually shifts its focus towards minor classes for classification during the training progress. Experimental results demonstrate that our approach outperforms the state-of-the-art works on the VQA-CP v2.0 dataset without requiring additional annotations. Notably, on the “num” type, our framework exceeds the second-best method (without extra annotations) by 8.64%. Meanwhile, our approach does not sacrifice accuracy performance on the VQA v2.0 dataset compared with the baseline model.

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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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