125 citations · 199 across the 13 of their papers we have counts for
13 papers
T2A-Feedback: Improving Basic Capabilities of Text-to-Audio Generation via Fine-grained AI Feedback
Zehan Wang, Ke Lei, Chen Zhu +8
Text-to-audio (T2A) generation has achieved remarkable progress in generating a variety of audio outputs from language prompts. However, current state-of-the-art T2A models still s…
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…
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…
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…
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…
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…
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…
Detector Guidance for Multi-Object Text-to-Image Generation
Luping Liu, Zijian Zhang, Yi Ren +3
Diffusion models have demonstrated impressive performance in text-to-image generation. They utilize a text encoder and cross-attention blocks to infuse textual information into ima…
PTQD: Accurate Post-Training Quantization for Diffusion Models
Yefei He, Luping Liu, Jing Liu +3
Diffusion models have recently dominated image synthesis tasks. However, the iterative denoising process is expensive in computations at inference time, making diffusion models les…
Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models
Rongjie Huang, Jiawei Huang, Dongchao Yang +7
Large-scale multimodal generative modeling has created milestones in text-to-image and text-to-video generation. Its application to audio still lags behind for two main reasons: th…
ERA-Solver: Error-Robust Adams Solver for Fast Sampling of Diffusion Probabilistic Models
Shengming Li, Luping Liu, Runnan Li +1
Though denoising diffusion probabilistic models (DDPMs) have achieved remarkable generation results, the low sampling efficiency of DDPMs still limits further applications. Since D…
Diffusion Denoising Process for Perceptron Bias in Out-of-distribution Detection
Luping Liu, Yi Ren, Xize Cheng +3
Out-of-distribution (OOD) detection is a crucial task for ensuring the reliability and safety of deep learning. Currently, discriminator models outperform other methods in this reg…
Pseudo Numerical Methods for Diffusion Models on Manifolds
Luping Liu, Yi Ren, Zhijie Lin +1
Denoising Diffusion Probabilistic Models (DDPMs) can generate high-quality samples such as image and audio samples. However, DDPMs require hundreds to thousands of iterations to pr…