6 accepted papers
Recent works in Gaussian Splatting (GS) SLAM highlight the importance of geometric structure. However, existing methods often rely on either 2D or 3D Gaussian primitives, lacking the balance between geometry and appearance, thus failing to precisely model spatial structures. Furthermore, current fra
Mixture-of-Experts (MoE) large language models improve inference efficiency through sparse expert activation, but deployment on resource-constrained devices remains challenging due to the large expert parameter footprint. Expert offloading mitigates this issue by loading experts on demand, yet its e
Single sensor (visual or LiDAR) simultaneous localization and mapping (SLAM) is fragile in the complex environment, which makes visual-LiDAR fusion a mainstream in SLAM research. However, most existing fusion methods omit explicit modeling of feature uncertainties and do not quantify each feature's …
It remains a tough challenge to tackle sound event localization and detection (SELD) problem, especially when sound scene complexity increases and overlapping acoustic sources appear. To improve the SELD performance, we propose an ensemble system, which consists of a ResNet and Conformer backbone ne…
Due to the rising concern of data privacy, it's reasonable to assume the local client data can't be transferred to a centralized server, nor their associated identity label is provided. To support continuous learning and fill the last-mile quality gap, we introduce a new problem setup called Local-A…
The key challenge for 3D face shape reconstruction is to build the correct dense face correspondence between the deformable mesh and the single input image. Given the ill-posed nature, previous works heavily rely on prior knowledge (such as 3DMM [2]) to reduce depth ambiguity. Although impressive re…