  To interpret deep models' predictions, attention-based visual cues are widely
used in addressing \textit{why} deep models make such predictions. Beyond that,
the current research community becomes more interested in reasoning
\textit{how} deep models make predictions, where some prototype-based methods
employ interpretable representations with their corresponding visual cues to
reveal the black-box mechanism of deep model behaviors. However, these
pioneering attempts only either learn the category-specific prototypes and
deteriorate their generalizing capacities, or demonstrate several illustrative
examples without a quantitative evaluation of visual-based interpretability
with further limitations on their practical usages. In this paper, we revisit
the concept of visual words and propose the Learnable Visual Words (LVW) to
interpret the model prediction behaviors with two novel modules: semantic
visual words learning and dual fidelity preservation. The semantic visual words
learning relaxes the category-specific constraint, enabling the general visual
words shared across different categories. Beyond employing the visual words for
prediction to align visual words with the base model, our dual fidelity
preservation also includes the attention guided semantic alignment that
encourages the learned visual words to focus on the same conceptual regions for
prediction. Experiments on six visual benchmarks demonstrate the superior
effectiveness of our proposed LVW in both accuracy and model interpretation
over the state-of-the-art methods. Moreover, we elaborate on various in-depth
analyses to further explore the learned visual words and the generalizability
of our method for unseen categories.
