Asia
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning Qi Wang 1, 2 Junming Y ang 3 Y unbo Wang
Training offline RL models using visual inputs poses two significant challenges, i.e.,
SfPUEL: Shape from Polarization under Unknown Environment Light
DeepSfP (4), which is even comparable with the multiview SfP method P ANDORA (15). In addition, metallic and dielectric surfaces exhibit different polarization BRDFs under the same illumination, which causes AoLP maps to vary on different materials, further compounding the normal estimation problem.
ID-to-3D: Expressive ID-guided 3D Heads via Score Distillation Sampling
From left to right: a) renderings, b) input images, c) normals.
A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences
Such causal graphs delineate the relations among alarms and can significantly aid engineers in identifying and rectifying faults. However, existing methods either ignore the topological relationships among devices or suffer from relatively low scalability and efficiency, failing to deliver high-quality responses in a timely manner.
4D Panoptic Scene Graph Generation Jingkang Y ang
Traditional 3D scene graph methods may recognize the static elements of this scene, such as identifying a booth situated on the ground. However, a more ideal, advanced, and dynamic perception is required for real-world scenarios.
FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective Kun Yi
Multivariate time series (MTS) forecasting has shown great importance in numerous industries.
Supplementary Material Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction
These metals are supposed as Titanium. Detailed parameters of the acquisition geometry can be found in Table 1. This sample is 3D cone-beam data. The estimated spectrum is illustrated in Figure 1 ( Right). 2 2 Additional Details of Baselines In our experiments, we compare our proposed method against eight baseline MAR approaches. Specifically, it learns the prior distribution of metal-free CT images with a generative model in order to infer the lost sinogram in the metal-affected regions.