Model-Based Data-Centric AI: Bridging the Divide Between Academic Ideals and Industrial Pragmatism

This paper delves into the contrasting roles of data within academic and industrial spheres, highlighting the divergence between Data-Centric AI and Model-Agnostic AI approaches. We argue that while Data-Centric AI focuses on the primacy of high-quality data for model performance, Model-Agnostic AI prioritizes algorithmic flexibility, often at the expense of data quality considerations. This distinction reveals that academic standards for data quality frequently do not meet the rigorous demands of industrial applications, leading to potential pitfalls in deploying academic models in real-world settings. Through a comprehensive analysis, we address these disparities, presenting both the challenges they pose and strategies for bridging the gap. Furthermore, we propose a novel paradigm: Model-Based Data-Centric AI, which aims to reconcile these differences by integrating model considerations into data optimization processes. This approach underscores the necessity for evolving data requirements that are sensitive to the nuances of both academic research and industrial deployment. By exploring these discrepancies, we aim to foster a more nuanced understanding of data's role in AI development and encourage a convergence of academic and industrial standards to enhance AI's real-world applicability.

SRFormer: Empowering Regression-Based Text Detection Transformer with Segmentation

Existing techniques for text detection can be broadly classified into two primary groups: segmentation-based methods and regression-based methods. Segmentation models offer enhanced robustness to font variations but require intricate post-processing, leading to high computational overhead. Regression-based methods undertake instance-aware prediction but face limitations in robustness and data efficiency due to their reliance on high-level representations. In our academic pursuit, we propose SRFormer, a unified DETR-based model with amalgamated Segmentation and Regression, aiming at the synergistic harnessing of the inherent robustness in segmentation representations, along with the straightforward post-processing of instance-level regression. Our empirical analysis indicates that favorable segmentation predictions can be obtained at the initial decoder layers. In light of this, we constrain the incorporation of segmentation branches to the first few decoder layers and employ progressive regression refinement in subsequent layers, achieving performance gains while minimizing additional computational load from the mask. Furthermore, we propose a Mask-informed Query Enhancement module. We take the segmentation result as a natural soft-ROI to pool and extract robust pixel representations, which are then employed to enhance and diversify instance queries. Extensive experimentation across multiple benchmarks has yielded compelling findings, highlighting our method's exceptional robustness, superior training and data efficiency, as well as its state-of-the-art performance.