The imperative to comprehend the behaviors of deep learning models is of utmost importance. In this realm, Explainable Artificial Intelligence (XAI) has emerged as a promising avenue, garnering increasing interest in recent years. Despite this, most existing methods primarily depend on gradients or input perturbation, which often fails to embed explanations directly within the model's decision-making process. Addressing this gap, we introduce ESCOUTER, a visually explainable classifier based on the modified slot attention mechanism. ESCOUTER distinguishes itself by not only delivering high classification accuracy but also offering more transparent insights into the reasoning behind its decisions. It differs from prior approaches in two significant aspects: (a) ESCOUTER incorporates explanations into the final confidence scores for each category, providing a more intuitive interpretation, and (b) it offers positive or negative explanations for all categories, elucidating "why an image belongs to a certain category" or "why it does not." A novel loss function specifically for ESCOUTER is designed to fine-tune the model's behavior, enabling it to toggle between positive and negative explanations. Moreover, an area loss is also designed to adjust the size of the explanatory regions for a more precise explanation. Our method, rigorously tested across various datasets and XAI metrics, outperformed previous state-of-the-art methods, solidifying its effectiveness as an explanatory tool.

ESCOUTER是一种基于改进的槽注意机制的可视化可解释分类器，不仅提供高分类准确性，还能提供对决策过程背后推理的更透明的洞察。它通过将解释融入每个类别的最终置信度得分，为每个类别提供更直观的解释，同时为所有类别提供正面或负面解释，以阐明图像属于某一类别的原因或不属于某一类别的原因。通过精确调整解释区域的大小，以提供更精确的解释，并使用特定于ESCOUTER的损失函数进行模型行为的微调，在各种数据集和XAI指标的严格测试中，我们的方法优于以往的最先进方法，巩固了它作为解释工具的有效性。

增强型槽注意力分类器的可解释图像识别