Model-Agnostic and Efficient Mixup Augmentation Guided by Saliency Maps.

Abstract

Recent Mixup-based data augmentation methods have integrated saliency information for richer supervisory signals. However, they often face significant computational burdens, require additional modules, or are constrained by specific architectures. To overcome these limitations, we present GuidedMixup, a model-agnostic, saliency-aware mixup strategy. Unlike previous methods that struggle with random pairings of discordant source and target images, we focus on matching harmonious pairs among mini-batch images and develop an efficient algorithm to identify image pairs with minimal conflict in salient regions. Thanks to these effective pairs, GuidedMixup employs simplified but fine-grained mask generation and adjusts the pixel-wise mixing ratio based solely on the relative saliency strength of paired images, avoiding complex optimization. Additionally, we introduce GuidedMixup++, which incorporates an optimal location search for efficiently relocating target images. GuidedMixup++ resizes target images and calculates minimal conflict for each pair candidate by considering all possible positions of target images, which is remarkably efficient powered by convolution operations. This information is then used to select pairs for mixing. Experimental results demonstrate that the proposed methods surpass other saliency-based techniques in terms of efficiency, generalization performance, and robustness against corrupted or reduced datasets, as well as in downstream tasks like object detection and instance segmentation.