5 accepted papers
3D reconstruction from in-the-wild images remains a challenging task due to inconsistent lighting conditions and transient distractors. Existing methods typically rely on heuristic strategies to handle the low-quality training data, which often struggle to produce stable and consistent reconstructio…
Existing NeRF-based methods for large scene reconstruction often have limitations in visual quality and rendering speed. While the recent 3D Gaussian Splatting works well on small-scale and object-centric scenes scaling it up to large scenes poses challenges due to limited video memory long optimiza…
A family of methods that generate soft labels by mixing the hard labels with a certain distribution, namely label refurbishment, are widely used to train deep neural networks. However, some of these methods are still poorly understood in the presence of label noise. In this paper, we revisit four la…
Machine learning has been highly successful in data-driven applications but is often hampered when the data contains noise, especially label noise. When trained on noisy labels, deep neural networks tend to fit all noisy labels, resulting in poor generalization. To handle this problem, a common idea…
Deep neural networks are prone to overfitting noisy labels, resulting in poor generalization performance. To overcome this problem, we present a simple and effective method self-ensemble label correction (SELC) to progressively correct noisy labels and refine the model. We look deeper into the memor…