BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of Attention

TL;DR

BR-NPA introduces a non-parametric attention mechanism that produces high-resolution, interpretable attention maps, enhancing the understanding of model decisions across various classification tasks without sacrificing accuracy.

Contribution

It proposes a novel bilinear non-parametric attention method that improves interpretability by generating finer-grained, task-relevant attention maps through feature grouping and ranking.

Findings

More accurate visual explanations than state-of-the-art methods

Applicable across multiple tasks including fine-grained classification and re-identification

Maintains classification accuracy while improving interpretability

Abstract

The prevalence of employing attention mechanisms has brought along concerns on the interpretability of attention distributions. Although it provides insights about how a model is operating, utilizing attention as the explanation of model predictions is still highly dubious. The community is still seeking more interpretable strategies for better identifying local active regions that contribute the most to the final decision. To improve the interpretability of existing attention models, we propose a novel Bilinear Representative Non-Parametric Attention (BR-NPA) strategy that captures the task-relevant human-interpretable information. The target model is first distilled to have higher-resolution intermediate feature maps. From which, representative features are then grouped based on local pairwise feature similarity, to produce finer-grained, more precise attention maps highlighting task-relevant parts of the input. The obtained attention maps are ranked according to the activity level of the compound feature, which provides information regarding the important level of the highlighted regions. The proposed model can be easily adapted in a wide variety of modern deep models, where classification is involved. Extensive quantitative and qualitative experiments showcase more comprehensive and accurate visual explanations compared to state-of-the-art attention models and visualizations methods across multiple tasks including fine-grained image classification, few-shot classification, and person re-identification, without compromising the classification accuracy. The proposed visualization model sheds imperative light on how neural networks `pay their attention' differently in different tasks.