The content discusses the challenges of fine-tuning stereo matching networks with synthetic data pre-training in real-world scenarios. It introduces the concept of Ground Truth (GT) versus Pseudo Label (PL) for fine-tuning, highlighting the importance of regularization and preventing overfitting. The DKT framework is proposed to address these issues by utilizing an EMA Teacher to enhance GT and PL during fine-tuning, resulting in improved robustness and generalization across various datasets.
The experiments conducted on different datasets show that using PL can preserve domain generalization ability, while GT supervision is crucial for target-domain performance. Fine-grained permutations added by the EMA Teacher help prevent overfitting details in GT. Comparisons with other methods demonstrate the effectiveness of DKT in maintaining robustness during fine-tuning.
Key metrics such as error percentages are used to evaluate performance on different datasets, showcasing the benefits of using GT and PL effectively in stereo matching networks. Visualizations and ablation studies further support the findings, emphasizing the significance of dynamic adjustments based on what networks have learned.
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by Jiawei Zhang... um arxiv.org 03-13-2024
https://arxiv.org/pdf/2403.07705.pdfTiefere Fragen