5 accepted papers
Long-term time series forecasting (LTSF) plays a crucial role in fields such as energy management, finance, and traffic prediction. Transformer-based models have adopted patch-based strategies to capture long-range dependencies, but accurately modeling shape similarities across patches and variables…
Deep neural networks for medical image segmentation often produce overconfident predictions, posing clinical risks due to miscalibrated uncertainty estimates. In this work, we rethink model calibration from a frequency-domain perspective and identify two critical factors causing miscalibration: spec…
We present Continuous-Time Attention, a novel framework that infuses partial differential equations (PDEs) into the Transformer’s attention mechanism to better handle long sequences. Instead of relying on a static attention matrix, we allow attention weights to evolve along a pseudo-time dimension g
The short video has gained increasing attention in information sharing and commercial promotions due to the fast development of social platforms. Accompanying, it introduces great requirements for assessing the quality of short videos for efficient information acquirement and propagation. However, e…
We present CO-Net, a cohesive framework that optimizes multiple point cloud tasks collectively across heterogeneous dataset domains. CO-Net maintains the characteristics of high storage efficiency since models with the preponderance of shared parameters can be assembled into a single model. Specific…