# ResizeMix: Mixing Data with Preserved Object Information and True Labels

arXiv: https://arxiv.org/abs/2012.11101 · HTML: https://mlsanity.com/paper/2012.11101/

## Key facts

- Cited by 69 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

## Built on by (most cited first, 30 of 69)

- [High-Performance Large-Scale Image Recognition Without Normalization](https://mlsanity.com/paper/2102.06171/) (2021)
- [AutoMix: Unveiling the Power of Mixup for Stronger Classifiers](https://mlsanity.com/paper/2103.13027/) (2021)
- [TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers](https://mlsanity.com/paper/2207.08409/) (2022)
- [Playing the Fool: Jailbreaking LLMs and Multimodal LLMs with Out-of-Distribution Strategy](https://mlsanity.com/paper/2503.20823/) (2025)
- [ExcelFormer: A neural network surpassing GBDTs on tabular data](https://mlsanity.com/paper/2301.02819/) (2023)
- [AlignMixup: Improving Representations By Interpolating Aligned Features](https://mlsanity.com/paper/2103.15375/) (2021)
- [MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks](https://mlsanity.com/paper/2103.06132/) (2021)
- [A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability](https://mlsanity.com/paper/2212.10888/) (2022)
- [Topology-aware Convolutional Neural Network for Efficient Skeleton-based Action Recognition](https://mlsanity.com/paper/2112.04178/) (2021)
- [A Simple Data Mixing Prior for Improving Self-Supervised Learning](https://mlsanity.com/paper/2206.07692/) (2022)
- [A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective](https://mlsanity.com/paper/2208.09913/) (2022)
- [Survey: Image Mixing and Deleting for Data Augmentation](https://mlsanity.com/paper/2106.07085/) (2021)
- [It Takes Two to Tango: Mixup for Deep Metric Learning](https://mlsanity.com/paper/2106.04990/) (2021)
- [Image Data Augmentation Approaches: A Comprehensive Survey and Future directions](https://mlsanity.com/paper/2301.02830/) (2023)
- [Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup](https://mlsanity.com/paper/2111.15454/) (2021)
- [OpenMixup: Open Mixup Toolbox and Benchmark for Visual Representation Learning](https://mlsanity.com/paper/2209.04851/) (2022)
- [A Survey on Mixup Augmentations and Beyond](https://mlsanity.com/paper/2409.05202/) (2024)
- [RankMixup: Ranking-Based Mixup Training for Network Calibration](https://mlsanity.com/paper/2308.11990/) (2023)
- [Harnessing Hard Mixed Samples with Decoupled Regularizer](https://mlsanity.com/paper/2203.10761/) (2022)
- [Adversarial AutoMixup](https://mlsanity.com/paper/2312.11954/) (2023)
- [Few-Shot Recognition via Stage-Wise Retrieval-Augmented Finetuning](https://mlsanity.com/paper/2406.11148/) (2024)
- [GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing](https://mlsanity.com/paper/2412.02366/) (2024)
- [SUMix: Mixup with Semantic and Uncertain Information](https://mlsanity.com/paper/2407.07805/) (2024)
- [Context-guided Responsible Data Augmentation with Diffusion Models](https://mlsanity.com/paper/2503.10687/) (2025)
- [RandoMix: A mixed sample data augmentation method with multiple mixed modes](https://mlsanity.com/paper/2205.08728/) (2022)
- [SMMix: Self-Motivated Image Mixing for Vision Transformers](https://mlsanity.com/paper/2212.12977/) (2022)
- [MergeMix: A Unified Augmentation Paradigm for Visual and Multi-Modal Understanding](https://mlsanity.com/paper/2510.23479/) (2025)
- [LUMix: Improving Mixup by Better Modelling Label Uncertainty](https://mlsanity.com/paper/2211.15846/) (2022)
- [Inter-Instance Similarity Modeling for Contrastive Learning](https://mlsanity.com/paper/2306.12243/) (2023)
- [BEM: Balanced and Entropy-based Mix for Long-Tailed Semi-Supervised Learning](https://mlsanity.com/paper/2404.01179/) (2024)

## Abstract (arXiv)

Data augmentation is a powerful technique to increase the diversity of data, which can effectively improve the generalization ability of neural networks in image recognition tasks. Recent data mixing based augmentation strategies have achieved great success. Especially, CutMix uses a simple but effective method to improve the classifiers by randomly cropping a patch from one image and pasting it on another image. To further promote the performance of CutMix, a series of works explore to use the saliency information of the image to guide the mixing. We systematically study the importance of the saliency information for mixing data, and find that the saliency information is not so necessary for promoting the augmentation performance. Furthermore, we find that the cutting based data mixing methods carry two problems of label misallocation and object information missing, which cannot be resolved simultaneously. We propose a more effective but very easily implemented method, namely ResizeMix. We mix the data by directly resizing the source image to a small patch and paste it on another image. The obtained patch preserves more substantial object information compared with conventional cut-based methods. ResizeMix shows evident advantages over CutMix and the saliency-guided methods on both image classification and object detection tasks without additional computation cost, which even outperforms most costly search-based automatic augmentation methods.