cvpr submission

sec/conclusion.tex

@@ -1,21 +1,11 @@
 % !TeX root = ../main.tex
 
-\section{Conclusion \& Future Work}
+\section{Discussion \& Conclusion}
 \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.
+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.