Search Results for author:
Found 8 papers, 3 papers with code
ComposeAnyone: Controllable Layout-to-Human Generation with Decoupled Multimodal Conditions
Building on the success of diffusion models, significant advancements have been made in multimodal image generation tasks.
Orca: Ocean Significant Wave Height Estimation with Spatio-temporally Aware Large Language Models
By segmenting the limited buoy observational data temporally, encoding the buoys' locations spatially, and designing prompt templates, Orca capitalizes on the robust generalization ability of LLMs to estimate significant wave height effectively with limited data.
AutoStudio: Crafting Consistent Subjects in Multi-turn Interactive Image Generation
As cutting-edge Text-to-Image (T2I) generation models already excel at producing remarkable single images, an even more challenging task, i. e., multi-turn interactive image generation begins to attract the attention of related research communities.
TheaterGen: Character Management with LLM for Consistent Multi-turn Image Generation
To address this issue, we introduce TheaterGen, a training-free framework that integrates large language models (LLMs) and text-to-image (T2I) models to provide the capability of multi-turn image generation.
Deep Learning Based Multi-Node ISAC 4D Environmental Reconstruction with Uplink- Downlink Cooperation
Utilizing widely distributed communication nodes to achieve environmental reconstruction is one of the significant scenarios for Integrated Sensing and Communication (ISAC) and a crucial technology for 6G.
Integrated Sensing and Communication enabled Multiple Base Stations Cooperative UAV Detection
Specifically, a multiple signal classification (MUSIC)-based symbol-level fusion method is proposed for UAV localization and velocity estimation, consisting of a single-BS preprocessing step and a lattice points searching step.
Ligandformer: A Graph Neural Network for Predicting Compound Property with Robust Interpretation
Robust and efficient interpretation of QSAR methods is quite useful to validate AI prediction rationales with subjective opinion (chemist or biologist expertise), understand sophisticated chemical or biological process mechanisms, and provide heuristic ideas for structure optimization in pharmaceutical industry.
Identifying Gene-environment interactions with robust marginal Bayesian variable selection
In this study, we propose a novel marginal Bayesian variable selection method for G$\times$E studies.
Variable Selection
Methodology
