4 citations · 11 across the 13 of their papers we have counts for
13 papers
Accelerating Disaggregated RL for Visual Generative LLMs with Diffusion-Based Parallelism and Trainer-Assisted Generation
Sijie Wang, Zhengyu Qing, Zhiqiang Tan +6
Reinforcement learning (RL) has become a dominant post-training paradigm, driving the emergence of high-performance RL systems such as veRL for autoregressive large language models…
Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models
Longteng Zhang, Sen Wu, Shuai Hou +7
Adapting large pre-trained language models to downstream tasks often entails fine-tuning millions of parameters or deploying costly dense weight updates, which hinders their use in…
SDG-Track: A Heterogeneous Observer-Follower Framework for High-Resolution UAV Tracking on Embedded Platforms
Jiawen Wen, Yu Hu, Suixuan Qiu +2
Real-time tracking of small unmanned aerial vehicles (UAVs) on edge devices faces a fundamental resolution-speed conflict. Downsampling high-resolution imagery to standard detector…
Towards Universal Video Retrieval: Generalizing Video Embedding via Synthesized Multimodal Pyramid Curriculum
Zhuoning Guo, Mingxin Li, Yanzhao Zhang +3
The prevailing video retrieval paradigm is structurally misaligned, as narrow benchmarks incentivize correspondingly limited data and single-task training. Therefore, universal cap…
City-VLM: Towards Multidomain Perception Scene Understanding via Multimodal Incomplete Learning
Penglei Sun, Yaoxian Song, Xiangru Zhu +7
Scene understanding enables intelligent agents to interpret and comprehend their environment. While existing large vision-language models (LVLMs) for scene understanding have prima…
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…
Dissecting the NVIDIA Hopper Architecture through Microbenchmarking and Multiple Level Analysis
Weile Luo, Ruibo Fan, Zeyu Li +4
This study presents a comprehensive multi-level analysis of the NVIDIA Hopper GPU architecture, focusing on its performance characteristics and novel features. We benchmark Hopper'…
Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey
Dayou Du, Gu Gong, Xiaowen Chu
Vision Transformers (ViTs) have recently garnered considerable attention, emerging as a promising alternative to convolutional neural networks (CNNs) in several vision-related appl…
Benchmarking and Dissecting the Nvidia Hopper GPU Architecture
Weile Luo, Ruibo Fan, Zeyu Li +3
Graphics processing units (GPUs) are continually evolving to cater to the computational demands of contemporary general-purpose workloads, particularly those driven by artificial i…
BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation
Dayou Du, Yijia Zhang, Shijie Cao +4
The upscaling of Large Language Models (LLMs) has yielded impressive advances in natural language processing, yet it also poses significant deployment challenges. Weight quantizati…
CF-NeRF: Camera Parameter Free Neural Radiance Fields with Incremental Learning
Qingsong Yan, Qiang Wang, Kaiyong Zhao +4
Neural Radiance Fields (NeRF) have demonstrated impressive performance in novel view synthesis. However, NeRF and most of its variants still rely on traditional complex pipelines t…
A Survey and Measurement Study of GPU DVFS on Energy Conservation
Xinxin Mei, Qiang Wang, Xiaowen Chu
Energy efficiency has become one of the top design criteria for current computing systems. The dynamic voltage and frequency scaling (DVFS) has been widely adopted by laptop comput…