Understanding how images influence the world, interpreting which effects
their semantics have on various quantities and exploring the reasons behind
changes in image-based predictions are highly difficult yet extremely
interesting problems. By adopting a holistic modeling approach utilizing Neural
Additive Models in combination with Diffusion Autoencoders, we can effectively
identify the latent hidden semantics of image effects and achieve full
intelligibility of additional tabular effects. Our approach offers a high
degree of flexibility, empowering us to comprehensively explore the impact of
various image characteristics. We demonstrate that the proposed method can
precisely identify complex image effects in an ablation study. To further
showcase the practical applicability of our proposed model, we conduct a case
study in which we investigate how the distinctive features and attributes
captured within host images exert influence on the pricing of Airbnb rentals.

通过综合建模方法，结合神经添加模型和扩散自编码器，我们能够有效识别图像效果的潜在隐藏语义并完全理解表格效果的影响。我们的方法具有高度灵活性，使我们能够全面探索各种图像特征的影响。通过消融实验，我们展示了该方法能够准确识别复杂的图像效果。为了进一步展示我们提出的模型的实际适用性，我们进行了一个案例研究，研究了主机图像中捕捉到的独特特征和属性对 Airbnb 租金定价的影响。