6 papers
EdgeFM: Efficient Edge Inference for Vision-Language Models
Mengling Deng, Yuanpeng Chen, Sheng Yang +12
Vision-language models (VLMs) have demonstrated strong applicability in edge industrial applications, yet their deployment remains severely constrained by requirements for determin…
Position: LLM Inference Should Be Evaluated as Energy-to-Token Production
Xiang Liu, Shimiao Yuan, Zhenheng Tang +5
LLM inference is still evaluated mainly as a model or software problem: accuracy, latency, throughput, and hardware utilization. This is incomplete. At deployment scale, the releva…
SpatialGrammar: A Domain-Specific Language for LLM-Based 3D Indoor Scene Generation
Song Tang, Kaiyong Zhao, Yuliang Li +5
Automatically generating interactive 3D indoor scenes from natural language is crucial for virtual reality, gaming, and embodied AI. However, existing LLM-based approaches often su…
RA-NeRF: Robust Neural Radiance Field Reconstruction with Accurate Camera Pose Estimation under Complex Trajectories
Qingsong Yan, Qiang Wang, Kaiyong Zhao +4
Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have emerged as powerful tools for 3D reconstruction and SLAM tasks. However, their performance depends heavily on ac…
SphereFusion: Efficient Panorama Depth Estimation via Gated Fusion
Qingsong Yan, Qiang Wang, Kaiyong Zhao +4
Due to the rapid development of panorama cameras, the task of estimating panorama depth has attracted significant attention from the computer vision community, especially in applic…
FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression
Zhenheng Tang, Xueze Kang, Yiming Yin +11
To alleviate hardware scarcity in training large deep neural networks (DNNs), particularly large language models (LLMs), we present FusionLLM, a decentralized training system desig…