first commit of eccv data

sec/conclusion.tex

@@ -0,0 +1,21 @@
+% !TeX root = ../main.tex
+
+\section{Conclusion \& Future Work}
+\label{sec:conclusion}
+
+% We introduce \schemename, a novel data augmentation scheme that facilitates improved Transformer training for image classification.
+% By explicitly separating and recombining foreground objects and backgrounds, \schemename enables controlled data augmentation beyond existing image compositions, leading to significant performance gains on ImageNet and downstream fine-grained classification tasks.
+% Furthermore, \schemename provides a powerful framework for analyzing model behavior and quantifying biases, including background robustness, foreground focus, center bias, and size bias.
+% Our experiments demonstrate that training using \schemename not only boosts accuracy but also significantly reduces these biases, resulting in more robust and generalizable models.
+% In the future, we see \schemename be also applied to other datasets and tasks, like video recognition or segmentation.
+% \schemename's ability to both improve performance and provide insights into model behavior makes it a valuable tool for advancing CV research and developing more reliable AI systems.
+
+
+We introduced \schemename, a controlled composition augmentation scheme that factorizes images into foreground objects and backgrounds and recombines them with explicit control over background identity, object position, and object scale.
+% Empirically, \schemename consistently improves clean accuracy and robustness across architectures and scales.
+Across diverse architectures, training with \schemename on top of standard strong augmentations yields substantial gains on ImageNet (up to $+6$ p.p.) and fine-grained downstream tasks (up to $+7.3$ p.p.), and consistently improves robustness on well-recognized benchmarks (up to $+19$ p.p.).
+\schemename's compositional controls additionally provide a framework for analyzing model behavior and quantify biases, including background robustness, foreground focus, center bias, and size bias.
+This dual role of \schemename as both a training mechanism and an evaluation tool highlights the value of explicit compositional factorization in understanding and improving image classifiers.
+In future work, we aim to extend controlled composition beyond classification to multi-object and dense prediction settings, including detection, segmentation, and video recognition.
+% By coupling performance gains with interpretable, controllable evaluations, \schemename offers a practical data-centric tool for advancing robust and reliable computer vision systems.
+More generally, we believe that designing augmentations around explicitly controllable and interpretable generative setups is a promising direction for building robust and reliable vision systems.