Automated Background Swapping for Robustness against Spurious Backgrounds

• URL: http://arxiv.org/abs/2606.32018v1
• Date: Tue, 30 Jun 2026 17:50:24 GMT
• Title: Automated Background Swapping for Robustness against Spurious Backgrounds
• Abstract summary: AutoBackSwap disentangles the foreground and background, followed by infilling to synthesize complete backgrounds, and finally combines different foregrounds and inpainted backgrounds to augment the training data.<n>We find that patch-wise labeling just a few hundred samples suffices to train the secondary network and automatically augment the full training on challenging image classification tasks.
• Score: 1.76179873429447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Classifiers based on Deep Neural Networks exhibit strong performance across domains, yet can fail catastrophically if they rely on spurious correlations, i.e., features that are predictive of the target label in the training data but are not causally linked and thus fail to generalize. For the vision domain, many such spurious correlations manifest themselves within the background of the image, where only the foreground is predictive of the class label. In this paper, we introduce Automated Background Swapping (AutoBackSwap) to reduce the reliance of classifiers on such spurious backgrounds. AutoBackSwap uses a secondary network to disentangle the foreground and background, followed by infilling to synthesize complete backgrounds, and finally combines different foregrounds and inpainted backgrounds to augment the training data. We find that patch-wise labeling of just a few hundred samples suffices to train the secondary network and automatically augment the full training dataset on challenging image classification tasks. In contrast to many previous methods, AutoBackSwap proves very effective even if there is not a single sample in the training data breaking the spurious correlation. Across a range of image classification tasks with spurious backgrounds, AutoBackSwap consistently outperforms prior methods.

Related papers

• Learning to Mask and Permute Visual Tokens for Vision Transformer Pre-Training [55.12082817901671]
  We propose a new self-supervised pre-training approach, named Masked and Permuted Vision Transformer (MaPeT)<n>MaPeT employs autoregressive and permuted predictions to capture intra-patch dependencies.<n>Our results demonstrate that MaPeT achieves competitive performance on ImageNet, compared to baselines and competitors under the same model setting.
  arXiv  Detail & Related papers  (2023-06-12T18:12:19Z)
• PRSNet: A Masked Self-Supervised Learning Pedestrian Re-Identification Method [2.0411082897313984]
  This paper designs a pre-task of mask reconstruction to obtain a pre-training model with strong robustness. The training optimization of the network is performed by improving the triplet loss based on the centroid. This method achieves about 5% higher mAP on Marker1501 and CUHK03 data than existing self-supervised learning pedestrian re-identification methods.
  arXiv  Detail & Related papers  (2023-03-11T07:20:32Z)
• Location-Aware Self-Supervised Transformers [74.76585889813207]
  We propose to pretrain networks for semantic segmentation by predicting the relative location of image parts. We control the difficulty of the task by masking a subset of the reference patch features visible to those of the query. Our experiments show that this location-aware pretraining leads to representations that transfer competitively to several challenging semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2022-12-05T16:24:29Z)
• Invariant Learning via Diffusion Dreamed Distribution Shifts [121.71383835729848]
  We propose a dataset called Diffusion Dreamed Distribution Shifts (D3S) D3S consists of synthetic images generated through StableDiffusion using text prompts and image guides obtained by pasting a sample foreground image onto a background template image. Due to the incredible photorealism of the diffusion model, our images are much closer to natural images than previous synthetic datasets.
  arXiv  Detail & Related papers  (2022-11-18T17:07:43Z)
• Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection [20.761672725633936]
  Training deep learning-based change detection model heavily depends on labeled data. Recent trend is using remote sensing (RS) data to obtain in-domain representations via supervised or self-supervised learning (SSL) We propose dense semantic-aware pre-training for RS image CD via sampling multiple class-balanced points.
  arXiv  Detail & Related papers  (2022-05-27T06:08:33Z)
• Enhanced Performance of Pre-Trained Networks by Matched Augmentation Distributions [10.74023489125222]
  We propose a simple solution to address the train-test distributional shift. We combine results for multiple random crops for a test image. This not only matches the train time augmentation but also provides the full coverage of the input image.
  arXiv  Detail & Related papers  (2022-01-19T22:33:00Z)
• Rectifying the Shortcut Learning of Background: Shared Object Concentration for Few-Shot Image Recognition [101.59989523028264]
  Few-Shot image classification aims to utilize pretrained knowledge learned from a large-scale dataset to tackle a series of downstream classification tasks. We propose COSOC, a novel Few-Shot Learning framework, to automatically figure out foreground objects at both pretraining and evaluation stage.
  arXiv  Detail & Related papers  (2021-07-16T07:46:41Z)
• Leveraging Self-Supervision for Cross-Domain Crowd Counting [71.75102529797549]
  State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. We train our network to recognize upside-down real images from regular ones and incorporate into it the ability to predict its own uncertainty. This yields an algorithm that consistently outperforms state-of-the-art cross-domain crowd counting ones without any extra computation at inference time.
  arXiv  Detail & Related papers  (2021-03-30T12:37:55Z)
• An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation [80.02124918255059]
  Semi-supervised learning aims to boost the accuracy of a model by exploring unlabeled images. We learn two networks to mutually teach each other. The more reliable predictions on easy images in each network are used to teach the other network to learn about the corresponding hard images.
  arXiv  Detail & Related papers  (2020-11-25T03:29:52Z)
• Background Splitting: Finding Rare Classes in a Sea of Background [55.03789745276442]
  We focus on the real-world problem of training accurate deep models for image classification of a small number of rare categories. In these scenarios, almost all images belong to the background category in the dataset (>95% of the dataset is background) We demonstrate that both standard fine-tuning approaches and state-of-the-art approaches for training on imbalanced datasets do not produce accurate deep models in the presence of this extreme imbalance.
  arXiv  Detail & Related papers  (2020-08-28T23:05:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.