AAAI Version

sec/abstract.tex

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 % !TeX root = ../main.tex
 
 \begin{abstract}
-    % Transformers, particularly Vision Transformers (ViTs), have achieved state-of-the-art performance in large-scale image classification.
-    % However, they often require large amounts of data and can exhibit biases, such as center or size bias, that limit their robustness and generalizability.
-    % This paper introduces \schemename, a novel data augmentation operation that addresses these challenges by explicitly imposing invariances into the training data, which are otherwise part of the neural network architecture.
-    % \schemename is constructed by using pretrained foundation models to separate and recombine foreground objects with different backgrounds.
-    % This recombination step enables us to take fine-grained control over object position and size, as well as background selection.
-    % We demonstrate that using \schemename significantly improves the accuracy of ViTs and other architectures by up to 4.5 percentage points (p.p.) on ImageNet, which translates to 7.3 p.p. on downstream tasks.
-    % Importantly, \schemename not only improves accuracy but also opens new ways to analyze model behavior and quantify biases.
-    % Namely, we introduce metrics for background robustness, foreground focus, center bias, and size bias and show that using \schemename during training substantially reduces these biases.
-    % In summary, \schemename provides a valuable tool for analyzing and mitigating biases, enabling the development of more robust and reliable computer vision models.
-    % Our code and dataset are publicly available at \code{<url>}.
-
-    Large-scale image classification datasets exhibit strong compositional biases: objects tend to be centered, appear at characteristic scales, and co-occur with class-specific context.
-    % Models can exploit these biases to achieve high in-distribution accuracy, yet remain brittle under distribution shifts.
-    By exploiting such biases, models attain high in-distribution accuracy but remain fragile under distribution shifts.
-    To address this issue, we introduce \schemename, a controlled composition augmentation scheme that factorizes each training image into a \emph{foreground object} and a \emph{background} and recombines them to explicitly manipulate object position, object scale, and background identity.
-    \schemename uses off-the-shelf segmentation and inpainting models to (i) extract the foreground and synthesize a neutral background, and (ii) paste the foreground onto diverse neutral backgrounds before applying standard strong augmentation policies.
-    Compared to conventional augmentations and content-mixing methods, our factorization provides direct control knobs that break foreground-background correlations. % while preserving the label.
-    Across 10 architectures, \schemename improves ImageNet top-1 accuracy by up to 6 percentage points (p.p.) and yields gains of up to 7.3 p.p. on fine-grained downstream datasets.
-    Moreover, the same control knobs enable targeted diagnostic tests: we quantify background reliance, foreground focus, center bias, and size bias via controlled background swaps and position/scale sweeps, and show that training with \schemename substantially reduces these shortcut behaviors and significantly increases accuracy on standard distribution-shift benchmarks by up to $19$ p.p.
-    % Moreover, the same control knobs enable targeted diagnostic tests: we quantify background reliance, foreground focus, center bias, and size bias via controlled background swaps and position/scale sweeps, and show that training with \schemename substantially reduces these shortcut behaviors and significantly increases accuracy on standard distribution-shift benchmarks like ImageNet-A/-C/-R by up to $19$ p.p.
+    Transformers, particularly Vision Transformers (ViTs), have achieved state-of-the-art performance in large-scale image classification.
+    However, they often require large amounts of data and can exhibit biases that limit their robustness and generalizability.
+    This paper introduces \schemename, a novel data augmentation scheme that addresses these challenges and explicitly includes inductive biases, which commonly are part of the neural network architecture, into the training data.
+    % This paper introduces \name, a novel dataset derived from ImageNet that addresses these challenges.
+    \schemename is constructed by using pretrained foundation models to separate and recombine foreground objects with different backgrounds, enabling fine-grained control over image composition during training.
+    It thus increases the data diversity and effective number of training samples.
+    We demonstrate that training on \name, the application of \schemename to ImageNet, significantly improves the accuracy of ViTs and other architectures by up to 4.5 percentage points (p.p.) on ImageNet and 7.3 p.p. on downstream tasks.
+    Importantly, \schemename enables novel ways of analyzing model behavior and quantifying biases.
+    Namely, we introduce metrics for background robustness, foreground focus, center bias, and size bias and show that training on \name substantially reduces these biases compared to training on ImageNet.
+    In summary, \schemename provides a valuable tool for analyzing and mitigating biases, enabling the development of more robust and reliable computer vision models.
     Our code and dataset are publicly available at \code{<url>}.
-
-    \keywords{Data Augmentation \and Vision Transformer \and Robustness}
-\end{abstract}
+\end{abstract}