TopoMix: Continuous Spatial Mixing for Long-Tailed Data Augmentation
Most mixing-based augmentation methods improve generalization through a simple idea: combine two training samples with a fixed global ratio or a hard spatial replacement. While effective, this design leaves little room for local variation, which becomes a more visible limitation in long-tailed recognition, where minority classes are especially sensitive to the quality of augmented samples. We propose TopoMix, a training-free augmentation method that replaces fixed mixing with continuous spatial mixing. TopoMix constructs a smooth spatially varying mask from a low-resolution random control grid, bicubic interpolation, and stochastic non-linear shaping, and uses this mask for element-wise image mixing and label reweighting. Without introducing trainable parameters or auxiliary networks, TopoMix offers a simple way to make mixing more flexible in the input space. Experiments on standard long-tailed benchmarks show that it consistently improves over representative mixing-based baselines, indicating that smooth spatial mixing is an effective augmentation strategy for long-tailed recognition.