Search Results for author:
Found 3 papers, 0 papers with code
LASER: Tuning-Free LLM-Driven Attention Control for Efficient Text-conditioned Image-to-Animation
Revolutionary advancements in text-to-image models have unlocked new dimensions for sophisticated content creation, e. g., text-conditioned image editing, allowing us to edit the diverse images that convey highly complex visual concepts according to the textual guidance.
Efficient Tuning and Inference for Large Language Models on Textual Graphs
In this paper, we propose ENGINE, a parameter- and memory-efficient fine-tuning method for textual graphs with an LLM encoder.
GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning
These pre-trained models can be applied to various downstream Web applications, saving training time and improving downstream (target) performance.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
