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Found 11 papers, 8 papers with code
Automated Movie Generation via Multi-Agent CoT Planning
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.
MovieBench: A Hierarchical Movie Level Dataset for Long Video Generation
Recent advancements in video generation models, like Stable Video Diffusion, show promising results, but primarily focus on short, single-scene videos.
FastGL: A GPU-Efficient Framework for Accelerating Sampling-Based GNN Training at Large Scale
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.
TernaryLLM: Ternarized Large Language Model
The proposed OFF can incorporate semantic information and is insensitive to outliers.
SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World
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.
Unsupervised Hyperspectral Pansharpening via Low-rank Diffusion Model
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.
Probability-based Global Cross-modal Upsampling for Pansharpening
Pansharpening is an essential preprocessing step for remote sensing image processing.
$\rm A^2Q$: Aggregation-Aware Quantization for Graph Neural Networks
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.
Multi-Task Conditional Imitation Learning for Autonomous Navigation at Crowded Intersections
A multi-task conditional imitation learning framework is proposed to adapt both lateral and longitudinal control tasks for safe and efficient interaction.
Country Image in COVID-19 Pandemic: A Case Study of China
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
Different cost functions of traversability analysis are learned and tested at various scenes of capability in guiding the trajectory planning of different behaviors.
