Search Results for author: Zeyu Zhu

Found 11 papers, 8 papers with code

Automated Movie Generation via Multi-Agent CoT Planning

1 code implementation10 Mar 2025 Weijia Wu, Zeyu Zhu, Mike Zheng Shou

Existing long-form video generation frameworks lack automated planning, requiring manual input for storylines, scenes, cinematography, and character interactions, resulting in high costs and inefficiencies.

Video Generation

MovieBench: A Hierarchical Movie Level Dataset for Long Video Generation

1 code implementation CVPR 2025 Weijia Wu, MingYu Liu, Zeyu Zhu, Xi Xia, Haoen Feng, Wen Wang, Kevin Qinghong Lin, Chunhua Shen, Mike Zheng Shou

Recent advancements in video generation models, like Stable Video Diffusion, show promising results, but primarily focus on short, single-scene videos.

Video Generation

FastGL: A GPU-Efficient Framework for Accelerating Sampling-Based GNN Training at Large Scale

1 code implementation23 Sep 2024 Zeyu Zhu, Peisong Wang, Qinghao Hu, Gang Li, Xiaoyao Liang, Jian Cheng

However, through an in-depth analysis, we observe that the efficiency of existing sampling-based training frameworks is still limited due to the key bottlenecks lying in all three phases of sampling-based training, i. e., subgraph sample, memory IO, and computation.

SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World

1 code implementation20 Sep 2023 Xingting Yao, Qinghao Hu, Fei Zhou, Tielong Liu, Zitao Mo, Zeyu Zhu, Zhengyang Zhuge, Jian Cheng

In SpikingNeRF, each sampled point on the ray is matched to a particular time step and represented in a hybrid manner where the voxel grids are maintained as well.

NeRF

Unsupervised Hyperspectral Pansharpening via Low-rank Diffusion Model

1 code implementation18 May 2023 Xiangyu Rui, Xiangyong Cao, Li Pang, Zeyu Zhu, Zongsheng Yue, Deyu Meng

To address these issues, in this work, we propose a low-rank diffusion model for hyperspectral pansharpening by simultaneously leveraging the power of the pre-trained deep diffusion model and better generalization ability of Bayesian methods.

Pansharpening

$\rm A^2Q$: Aggregation-Aware Quantization for Graph Neural Networks

1 code implementation1 Feb 2023 Zeyu Zhu, Fanrong Li, Zitao Mo, Qinghao Hu, Gang Li, Zejian Liu, Xiaoyao Liang, Jian Cheng

Through an in-depth analysis of the topology of GNNs, we observe that the topology of the graph leads to significant differences between nodes, and most of the nodes in a graph appear to have a small aggregation value.

Quantization

Multi-Task Conditional Imitation Learning for Autonomous Navigation at Crowded Intersections

no code implementations21 Feb 2022 Zeyu Zhu, Huijing Zhao

A multi-task conditional imitation learning framework is proposed to adapt both lateral and longitudinal control tasks for safe and efficient interaction.

Autonomous Driving Autonomous Navigation +1

Country Image in COVID-19 Pandemic: A Case Study of China

1 code implementation12 Sep 2020 Huimin Chen, Zeyu Zhu, Fanchao Qi, Yining Ye, Zhiyuan Liu, Maosong Sun, Jianbin Jin

Therefore, in this study, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA)

Off-road Autonomous Vehicles Traversability Analysis and Trajectory Planning Based on Deep Inverse Reinforcement Learning

no code implementations16 Sep 2019 Zeyu Zhu, Nan Li, Ruoyu Sun, Huijing Zhao, Donghao Xu

Different cost functions of traversability analysis are learned and tested at various scenes of capability in guiding the trajectory planning of different behaviors.

Autonomous Vehicles reinforcement-learning +3

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