# Explaining with Counter Visual Attributes and Examples

arXiv: https://arxiv.org/abs/2001.09671 · HTML: https://mlsanity.com/paper/2001.09671/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2020.

## Built on by (most cited first, 5 of 5)

- [A Review on Explainability in Multimodal Deep Neural Nets](https://mlsanity.com/paper/2105.07878/) (2021)
- [Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions](https://mlsanity.com/paper/2306.05731/) (2023)
- [Hierarchical Explanations for Video Action Recognition](https://mlsanity.com/paper/2301.00436/) (2023)
- [Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries](https://mlsanity.com/paper/2203.16475/) (2022)
- [TAX: Tendency-and-Assignment Explainer for Semantic Segmentation with Multi-Annotators](https://mlsanity.com/paper/2302.09561/) (2023)

## Abstract (arXiv)

In this paper, we aim to explain the decisions of neural networks by utilizing multimodal information. That is counter-intuitive attributes and counter visual examples which appear when perturbed samples are introduced. Different from previous work on interpreting decisions using saliency maps, text, or visual patches we propose to use attributes and counter-attributes, and examples and counter-examples as part of the visual explanations. When humans explain visual decisions they tend to do so by providing attributes and examples. Hence, inspired by the way of human explanations in this paper we provide attribute-based and example-based explanations. Moreover, humans also tend to explain their visual decisions by adding counter-attributes and counter-examples to explain what is not seen. We introduce directed perturbations in the examples to observe which attribute values change when classifying the examples into the counter classes. This delivers intuitive counter-attributes and counter-examples. Our experiments with both coarse and fine-grained datasets show that attributes provide discriminating and human-understandable intuitive and counter-intuitive explanations.