4 papers
Training LLMs to be Better Text Embedders through Bidirectional Reconstruction
Chang Su, Dengliang Shi, Siyuan Huang +5
Large language models (LLMs) have increasingly been explored as powerful text embedders. Existing LLM-based text embedding approaches often leverage the embedding of the final toke…
X-MoGen: Unified Motion Generation across Humans and Animals
Xuan Wang, Kai Ruan, Liyang Qian +3
Text-driven motion generation has attracted increasing attention due to its broad applications in virtual reality, animation, and robotics. While existing methods typically model h…
Cost-Aware Routing for Efficient Text-To-Image Generation
Qinchan Li, Kenneth Chen, Changyue Su +3
Diffusion models are well known for their ability to generate a high-fidelity image for an input prompt through an iterative denoising process. Unfortunately, the high fidelity als…
BudgetFusion: Perceptually-Guided Adaptive Diffusion Models
Qinchan Li, Kenneth Chen, Changyue Su +1
Diffusion models have shown unprecedented success in the task of text-to-image generation. While these models are capable of generating high-quality and realistic images, the compl…