Search Results for author:
Found 6 papers, 2 papers with code
Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want
In this paper, we introduce the Draw-and-Understand project: a new model, a multi-domain dataset, and a challenging benchmark for visual prompting.
IDAdapter: Learning Mixed Features for Tuning-Free Personalization of Text-to-Image Models
Leveraging Stable Diffusion for the generation of personalized portraits has emerged as a powerful and noteworthy tool, enabling users to create high-fidelity, custom character avatars based on their specific prompts.
Cloud-Device Collaborative Learning for Multimodal Large Language Models
However, the deployment of these large-scale MLLMs on client devices is hindered by their extensive model parameters, leading to a notable decline in generalization capabilities when these models are compressed for device deployment.
Accretionary Learning with Deep Neural Networks
In this paper, we propose a new learning method named Accretionary Learning (AL) to emulate human learning, in that the set of objects to be recognized may not be pre-specified.
Spatiotemporal Attention Networks for Wind Power Forecasting
This model captures spatial correlations among wind farms and temporal dependencies of wind power time series.
A Books Recommendation Approach Based on Online Bookstore Data
In the era of information explosion, facing complex information, it is difficult for users to choose the information of interest, and businesses also need detailed information on ways to let the ad stand out.
