# SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition

arXiv: https://arxiv.org/abs/2009.06138 · HTML: https://mlsanity.com/paper/2009.06138/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 29 of 29)

- [Concept-based Explainable Artificial Intelligence: A Survey](https://mlsanity.com/paper/2312.12936/) (2023)
- [Learning Bottleneck Concepts in Image Classification](https://mlsanity.com/paper/2304.10131/) (2023)
- [Visual Attention Methods in Deep Learning: An In-Depth Survey](https://mlsanity.com/paper/2204.07756/) (2022)
- [Guided Slot Attention for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2303.08314/) (2023)
- [Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks](https://mlsanity.com/paper/2410.02331/) (2024)
- [Unlocking Slot Attention by Changing Optimal Transport Costs](https://mlsanity.com/paper/2301.13197/) (2023)
- [Comparing the Decision-Making Mechanisms by Transformers and CNNs via Explanation Methods](https://mlsanity.com/paper/2212.06872/) (2022)
- [Learning with Capsules: A Survey](https://mlsanity.com/paper/2206.02664/) (2022)
- [Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace](https://mlsanity.com/paper/2305.15775/) (2023)
- [Explainable Diabetic Retinopathy Detection and Retinal Image Generation](https://mlsanity.com/paper/2107.00296/) (2021)
- [CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis](https://mlsanity.com/paper/2411.00696/) (2024)
- [Allowing humans to interactively guide machines where to look does not always improve human-AI team's classification accuracy](https://mlsanity.com/paper/2404.05238/) (2024)
- [A Survey of Explainable AI in Deep Visual Modeling: Methods and Metrics](https://mlsanity.com/paper/2301.13445/) (2023)
- [Enhancing Interpretable Object Abstraction via Clustering-based Slot Initialization](https://mlsanity.com/paper/2308.11369/) (2023)
- [Concept Complement Bottleneck Model for Interpretable Medical Image Diagnosis](https://mlsanity.com/paper/2410.15446/) (2024)
- [Explaining Cross-Domain Recognition with Interpretable Deep Classifier](https://mlsanity.com/paper/2211.08249/) (2022)
- [Interpreting Vision and Language Generative Models with Semantic Visual Priors](https://mlsanity.com/paper/2304.14986/) (2023)
- [CA-Stream: Attention-based pooling for interpretable image recognition](https://mlsanity.com/paper/2404.14996/) (2024)
- [Explainable Image Recognition via Enhanced Slot-attention Based Classifier](https://mlsanity.com/paper/2407.05616/) (2024)
- [Athena: Retrieval-augmented Legal Judgment Prediction with Large Language Models](https://mlsanity.com/paper/2410.11195/) (2024)
- [UNO: Unifying One-stage Video Scene Graph Generation via Object-Centric Visual Representation Learning](https://mlsanity.com/paper/2509.06165/) (2025)
- [Memory Wrap: a Data-Efficient and Interpretable Extension to Image Classification Models](https://mlsanity.com/paper/2106.01440/) (2021)
- [Beyond Patches: Mining Interpretable Part-Prototypes for Explainable AI](https://mlsanity.com/paper/2504.12197/) (2025)
- [Slot Attention-based Feature Filtering for Few-Shot Learning](https://mlsanity.com/paper/2508.09699/) (2025)
- [MUFASA: A Multi-Layer Framework for Slot Attention](https://mlsanity.com/paper/2602.07544/) (2026)
- [Dual-Channel Reliable Breast Ultrasound Image Classification Based on Explainable Attribution and Uncertainty Quantification](https://mlsanity.com/paper/2401.03664/) (2024)
- [EquiCaps: Predictor-Free Pose-Aware Pre-Trained Capsule Networks](https://mlsanity.com/paper/2506.09895/) (2025)
- [Matching-Based Few-Shot Semantic Segmentation Models Are Interpretable by Design](https://mlsanity.com/paper/2511.18163/) (2025)
- [What-Where Transformer: A Slot-Centric Visual Backbone for Concurrent Representation and Localization](https://mlsanity.com/paper/2605.12021/) (2026)

## Abstract (arXiv)

Explainable artificial intelligence has been gaining attention in the past few years. However, most existing methods are based on gradients or intermediate features, which are not directly involved in the decision-making process of the classifier. In this paper, we propose a slot attention-based classifier called SCOUTER for transparent yet accurate classification. Two major differences from other attention-based methods include: (a) SCOUTER's explanation is involved in the final confidence for each category, offering more intuitive interpretation, and (b) all the categories have their corresponding positive or negative explanation, which tells "why the image is of a certain category" or "why the image is not of a certain category." We design a new loss tailored for SCOUTER that controls the model's behavior to switch between positive and negative explanations, as well as the size of explanatory regions. Experimental results show that SCOUTER can give better visual explanations in terms of various metrics while keeping good accuracy on small and medium-sized datasets.