# CNNs with Multi-Level Attention for Domain Generalization Source: [CNNs with Multi-Level Attention for Domain Generalization]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:22:26.632Z Authors: Aristotelis Ballas, Christos Diou Published: 2023-04-02T10:34:40.000Z ## Abstract In the past decade, deep convolutional neural networks have achieved significant success in image classification and ranking and have therefore found numerous applications in multimedia content retrieval. Still, these models suffer from performance degradation when neural networks are tested on out-of-distribution scenarios or on data originating from previously unseen data Domains. In the present work, we focus on this problem of Domain Generalization and propose an alternative neural network architecture for robust, out-of-distribution image classification. We attempt to produce a model that focuses on the causal features of the depicted class for robust image classification in the Domain Generalization setting. To achieve this, we propose attending to multiple-levels of information throughout a Convolutional Neural Network and leveraging the most important attributes of an image by employing trainable attention mechanisms. To validate our method, we evaluate our model on four widely accepted Domain Generalization benchmarks, on which our model is able to surpass previously reported baselines in three out of four datasets and achieve the second best score in the fourth one. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()