  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.
