32 citations · 97 across the 33 of their papers we have counts for
11 papers · 1 filter
PEA-Diffusion: Parameter-Efficient Adapter with Knowledge Distillation in non-English Text-to-Image Generation
Jian Ma, Chen Chen, Qingsong Xie +1
Text-to-image diffusion models are well-known for their ability to generate realistic images based on textual prompts. However, the existing works have predominantly focused on Eng…
Interactive Interior Design Recommendation via Coarse-to-fine Multimodal Reinforcement Learning
He Zhang, Ying Sun, Weiyu Guo +4
Personalized interior decoration design often incurs high labor costs. Recent efforts in developing intelligent interior design systems have focused on generating textual requireme…
MCAD: Multi-teacher Cross-modal Alignment Distillation for efficient image-text retrieval
Youbo Lei, Feifei He, Chen Chen +4
Due to the success of large-scale visual-language pretraining (VLP) models and the widespread use of image-text retrieval in industry areas, it is now critically necessary to reduc…
MoEController: Instruction-based Arbitrary Image Manipulation with Mixture-of-Expert Controllers
Sijia Li, Chen Chen, Haonan Lu
Diffusion-model-based text-guided image generation has recently made astounding progress, producing fascinating results in open-domain image manipulation tasks. Few models, however…
Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning
Jian Ma, Junhao Liang, Chen Chen +1
Recent progress in personalized image generation using diffusion models has been significant. However, development in the area of open-domain and non-fine-tuning personalized image…
Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models
Fobo Shi, Peijun Qing, Dong Yang +5
Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions. It enables LLMs to excel i…
Towards Language-guided Interactive 3D Generation: LLMs as Layout Interpreter with Generative Feedback
Yiqi Lin, Hao Wu, Ruichen Wang +4
Generating and editing a 3D scene guided by natural language poses a challenge, primarily due to the complexity of specifying the positional relations and volumetric changes within…
Compositional Text-to-Image Synthesis with Attention Map Control of Diffusion Models
Ruichen Wang, Zekang Chen, Chen Chen +3
Recent text-to-image (T2I) diffusion models show outstanding performance in generating high-quality images conditioned on textual prompts. However, they fail to semantically align…
Edit Everything: A Text-Guided Generative System for Images Editing
Defeng Xie, Ruichen Wang, Jian Ma +5
We introduce a new generative system called Edit Everything, which can take image and text inputs and produce image outputs. Edit Everything allows users to edit images using simpl…
GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation
Jian Ma, Mingjun Zhao, Chen Chen +4
Recent breakthroughs in the field of language-guided image generation have yielded impressive achievements, enabling the creation of high-quality and diverse images based on user i…
CompoNeRF: Text-guided Multi-object Compositional NeRF with Editable 3D Scene Layout
Haotian Bai, Yuanhuiyi Lyu, Lutao Jiang +4
Text-to-3D form plays a crucial role in creating editable 3D scenes for AR/VR. Recent advances have shown promise in merging neural radiance fields (NeRFs) with pre-trained diffusi…