5 accepted papers
Speculative decoding can significantly accelerate LLM inference, especially given that its cloud-edge collaborative deployment offers cloud workload offloading, offline robustness, and privacy enhancement. However, existing collaborative inference frameworks with speculative decoding are constrained…
Novel view synthesis of dynamic scenes is fundamental to achieving photorealistic 4D reconstruction and immersive visual experiences. Recent progress in Gaussian-based representations has significantly improved real-time rendering quality, yet existing methods still struggle to maintain a balance be
The parameter size of modern large language models (LLMs) can be scaled up to the trillion-level via the sparsely-activated Mixture-of-Experts (MoE) technique to avoid excessive increase of the computational costs. To further improve training efficiency, pipelining computation and communication has…
Various applications require high-fidelity and artifact-free 3D human reconstructions. However current implicit function-based methods inevitably produce artifacts while existing deformation methods are difficult to reconstruct high-fidelity humans wearing loose clothing. In this paper we propose a…
To solve the problem of inaccurate orientation estimation after long-term operations of the Inertial Measurement Unit (IMU), we present a learning-based method (called Gyro-Net) to estimate and compensate for IMU gyroscope random errors. We firstly introduce a semi-dense network structure, which ext