Search Results for author: Luping Liu

Found 14 papers, 8 papers with code

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

1 code implementation15 Oct 2024 Luping Liu, Chao Du, Tianyu Pang, Zehan Wang, Chongxuan Li, Dong Xu

To tackle these issues, we propose LongAlign, which includes a segment-level encoding method for processing long texts and a decomposed preference optimization method for effective alignment training.

OmniBind: Large-scale Omni Multimodal Representation via Binding Spaces

no code implementations16 Jul 2024 Zehan Wang, Ziang Zhang, Hang Zhang, Luping Liu, Rongjie Huang, Xize Cheng, Hengshuang Zhao, Zhou Zhao

Given the foundational role of multimodal joint representation in understanding and generation pipelines, high-quality omni joint representations would be a step toward co-processing more diverse multimodal information.

FreeBind: Free Lunch in Unified Multimodal Space via Knowledge Fusion

1 code implementation8 May 2024 Zehan Wang, Ziang Zhang, Xize Cheng, Rongjie Huang, Luping Liu, Zhenhui Ye, Haifeng Huang, Yang Zhao, Tao Jin, Peng Gao, Zhou Zhao

In this work, we propose FreeBind, an idea that treats multimodal representation spaces as basic units, and freely augments pre-trained unified space by integrating knowledge from extra expert spaces via "space bonds".

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

no code implementations15 Oct 2023 Zijian Zhang, Luping Liu, Zhijie Lin, Yichen Zhu, Zhou Zhao

We propose the first unsupervised and learning-based method to identify interpretable directions in h-space of pre-trained diffusion models.

Extending Multi-modal Contrastive Representations

1 code implementation13 Oct 2023 Zehan Wang, Ziang Zhang, Luping Liu, Yang Zhao, Haifeng Huang, Tao Jin, Zhou Zhao

Inspired by recent C-MCR, this paper proposes Extending Multimodal Contrastive Representation (Ex-MCR), a training-efficient and paired-data-free method to flexibly learn unified contrastive representation space for more than three modalities by integrating the knowledge of existing MCR spaces.

3D Object Classification Representation Learning +1

Detector Guidance for Multi-Object Text-to-Image Generation

1 code implementation4 Jun 2023 Luping Liu, Zijian Zhang, Yi Ren, Rongjie Huang, Xiang Yin, Zhou Zhao

Previous works identify the problem of information mixing in the CLIP text encoder and introduce the T5 text encoder or incorporate strong prior knowledge to assist with the alignment.

Object object-detection +2

Make-A-Voice: Unified Voice Synthesis With Discrete Representation

no code implementations30 May 2023 Rongjie Huang, Chunlei Zhang, Yongqi Wang, Dongchao Yang, Luping Liu, Zhenhui Ye, Ziyue Jiang, Chao Weng, Zhou Zhao, Dong Yu

Various applications of voice synthesis have been developed independently despite the fact that they generate "voice" as output in common.

Singing Voice Synthesis Text to Speech +1

PTQD: Accurate Post-Training Quantization for Diffusion Models

1 code implementation NeurIPS 2023 Yefei He, Luping Liu, Jing Liu, Weijia Wu, Hong Zhou, Bohan Zhuang

To address these challenges, we propose a unified formulation for the quantization noise and diffusion perturbed noise in the quantized denoising process.

Denoising Image Generation +1

ERA-Solver: Error-Robust Adams Solver for Fast Sampling of Diffusion Probabilistic Models

no code implementations30 Jan 2023 Shengmeng Li, Luping Liu, Zenghao Chai, Runnan Li, Xu Tan

Different from the traditional predictor based on explicit Adams methods, we leverage a Lagrange interpolation function as the predictor, which is further enhanced with an error-robust strategy to adaptively select the Lagrange bases with lower error in the estimated noise.

Denoising Image Generation

Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models

1 code implementation30 Jan 2023 Rongjie Huang, Jiawei Huang, Dongchao Yang, Yi Ren, Luping Liu, Mingze Li, Zhenhui Ye, Jinglin Liu, Xiang Yin, Zhou Zhao

Its application to audio still lags behind for two main reasons: the lack of large-scale datasets with high-quality text-audio pairs, and the complexity of modeling long continuous audio data.

Ranked #7 on Audio Generation on AudioCaps (FD metric)

Audio Generation Text-to-Video Generation +1

Diffusion Denoising Process for Perceptron Bias in Out-of-distribution Detection

no code implementations21 Nov 2022 Luping Liu, Yi Ren, Xize Cheng, Rongjie Huang, Chongxuan Li, Zhou Zhao

In this paper, we introduce a new perceptron bias assumption that suggests discriminator models are more sensitive to certain features of the input, leading to the overconfidence problem.

Denoising Out-of-Distribution Detection +1

A systematic review of fuzzing based on machine learning techniques

no code implementations4 Aug 2019 Yan Wang, Peng Jia, Luping Liu, Jiayong Liu

Next, this paper assesses the performance of the machine learning models based on the frequently used evaluation metrics.

BIG-bench Machine Learning

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