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20222025
most citedMake-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models

47 citations · 74 across the 12 of their papers we have counts for

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10 papers · 1 filter

cs.CV2024

Improving Long-Text Alignment for Text-to-Image Diffusion Models

Luping Liu, Chao Du, Tianyu Pang +3

The rapid advancement of text-to-image (T2I) diffusion models has enabled them to generate unprecedented results from given texts. However, as text inputs become longer, existing e…

cs.CV2024★ 1 cited

OmniBind: Large-scale Omni Multimodal Representation via Binding Spaces

Zehan Wang, Ziang Zhang, Hang Zhang +5

Recently, human-computer interaction with various modalities has shown promising applications, like GPT-4o and Gemini. Given the foundational role of multimodal joint representatio…

cs.CV2024

FreeBind: Free Lunch in Unified Multimodal Space via Knowledge Fusion

Zehan Wang, Ziang Zhang, Xize Cheng +8

Unified multi-model representation spaces are the foundation of multimodal understanding and generation. However, the billions of model parameters and catastrophic forgetting probl…

cs.CV2023★ 5 cited

Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers

Haifeng Huang, Yilun Chen, Zehan Wang +8

Recent advancements in 3D Large Language Models (LLMs) have demonstrated promising capabilities for 3D scene understanding. However, previous methods exhibit deficiencies in genera…

cs.CV2023★ 1 cited

Unsupervised Discovery of Interpretable Directions in h-space of Pre-trained Diffusion Models

Zijian Zhang, Luping Liu, Zhijie Lin +2

We propose the first unsupervised and learning-based method to identify interpretable directions in h-space of pre-trained diffusion models. Our method is derived from an existing…

cs.CV2023★ 2 cited

Extending Multi-modal Contrastive Representations

Zehan Wang, Ziang Zhang, Luping Liu +4

Multi-modal contrastive representation (MCR) of more than three modalities is critical in multi-modal learning. Although recent methods showcase impressive achievements, the high d…