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Pei Chen

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7 papers

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AAAI Conference 2026 Conference Paper

Diffusion Distillation with Direct Preference Optimization for Efficient 3D LiDAR Scene Completion

An Zhao
Shengyuan Zhang
Zejian Li
Ling Yang
Pei Chen
Jiale Wu
Haoran Xu
AnYang Wei

The slow sampling speed of diffusion models hinders their application in 3D LiDAR scene completion. To address this, we propose Distillation-DPO, a novel framework that accelerates sampling through score distillation while simultaneously enhancing generation quality via preference alignment. Distillation-DPO follows a three-step procedure. First, the student model generates paired completion scenes with different initial noises. Second, using LiDAR scene evaluation metrics as preference, we construct winning and losing sample pairs. Third, as our core innovation, Distillation-DPO optimizes the student model by exploiting the difference in score functions between the teacher and student models on the paired completion scenes. This operation performs variational score distillation of the student model but simultaneously encourages the distilled student to prefer the winning samples over the losing ones. Extensive experiments demonstrate that Distillation-DPO achieves higher-quality scene completion than state-of-the-art diffusion models, while accelerating sampling by over 5-fold. To our knowledge, our work is the first to integrate the preference learning principle of DPO into the distillation of diffusion models, offering a new framework of preference-aligned distillation.

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AAAI Conference 2025 Conference Paper

GVMGen: A General Video-to-Music Generation Model with Hierarchical Attentions

Heda Zuo
Weitao You
Junxian Wu
Shihong Ren
Pei Chen
Mingxu Zhou
Yujia Lu
Lingyun Sun

Composing music for video is essential yet challenging, leading to a growing interest in automating music generation for video applications. Existing approaches often struggle to achieve robust music-video correspondence and generative diversity, primarily due to inadequate feature alignment methods and insufficient datasets. In this study, we present General Video-to-Music Generation model (GVMGen), designed for generating high-related music to the video input. Our model employs hierarchical attentions to extract and align video features with music in both spatial and temporal dimensions, ensuring the preservation of pertinent features while minimizing redundancy. Remarkably, our method is versatile, capable of generating multi-style music from different video inputs, even in zero-shot scenarios. We also propose an evaluation model along with two novel objective metrics for assessing video-music alignment. Additionally, we have compiled a large-scale dataset comprising diverse types of video-music pairs. Experimental results demonstrate that GVMGen surpasses previous models in terms of music-video correspondence, music quality generative diversity, and application universality.

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IJCAI Conference 2025 Conference Paper

Intoner: For Chinese Poetry Intoning Synthesis

Heda Zuo
Liyao Sun
Zeyu Lai
Weitao You
Pei Chen
Lingyun Sun

Chinese Poetry Intoning, with improvised melodies devoid of fixed musical scores, is crucial for emotional expression and prosodic rendition. However, this cultural heritage faces challenges in propagation due to scant audio records and a scarcity of domain experts. Existing text-to-speech models lack the ability to generate melodious audio, while singing-voice-synthesis models rely on predetermined musical scores, which are all unsuitable for intoning synthesis. Hence, we introduce Chinese Poetry Intoning Synthesis (PIS) as a novel task to reproduce intoning audio and preserve this age-old cultural art. Corresponding to this task, we summarize three-level principles from poetry metrical patterns and construct a diffusion PIS model Intoner based on them. We also collect a multi-style Chinese poetry intoning dataset of text-audio pairs accompanied by feature annotations. Experimental results show that our model effectively learns diverse intoning styles and contents which can synthesize more melodious and vibrant intoning audio. To the best of our knowledge, we are the first to work on poetry intoning synthesis task.

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AAAI Conference 2025 Conference Paper

Personalized Dynamic Music Emotion Recognition with Dual-Scale Attention-Based Meta-Learning

Dengming Zhang
Weitao You
Ziheng Liu
Lingyun Sun
Pei Chen

Dynamic Music Emotion Recognition (DMER) aims to predict the emotion of different moments in music, playing a crucial role in music information retrieval. The existing DMER methods struggle to capture long-term dependencies when dealing with sequence data, which limits their performance. Furthermore, these methods often overlook the influence of individual differences on emotion perception, even though everyone has their own personalized emotional perception in the real world. Motivated by these issues, we explore more effective sequence processing methods and introduce the Personalized DMER (PDMER) problem, which requires models to predict emotions that align with personalized perception. Specifically, we propose a Dual-Scale Attention-Based Meta-Learning (DSAML) method. This method fuses features from a dual-scale feature extractor and captures both short and long-term dependencies using a dual-scale attention transformer, improving the performance in traditional DMER. To achieve PDMER, we design a novel task construction strategy that divides tasks by annotators. Samples in a task are annotated by the same annotator, ensuring consistent perception. Leveraging this strategy alongside meta-learning, DSAML can predict personalized perception of emotions with just one personalized annotation sample. Our objective and subjective experiments demonstrate that our method can achieve state-of-the-art performance in both traditional DMER and PDMER.

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NeurIPS Conference 2023 Conference Paper

HyTrel: Hypergraph-enhanced Tabular Data Representation Learning

Pei Chen
Soumajyoti Sarkar
Leonard Lausen
Balasubramaniam Srinivasan
Sheng Zha
Ruihong Huang
George Karypis

Language models pretrained on large collections of tabular data have demonstrated their effectiveness in several downstream tasks. However, many of these models do not take into account the row/column permutation invariances, hierarchical structure, etc. that exist in tabular data. To alleviate these limitations, we propose HyTrel, a tabular language model, that captures the permutation invariances and three more structural properties of tabular data by using hypergraphs--where the table cells make up the nodes and the cells occurring jointly together in each row, column, and the entire table are used to form three different types of hyperedges. We show thatHyTrel is maximally invariant under certain conditions for tabular data, i. e. , two tables obtain the same representations via HyTreliff the two tables are identical up to permutation. Our empirical results demonstrate that HyTrel consistently outperforms other competitive baselines on four downstream tasks with minimal pretraining, illustrating the advantages of incorporating inductive biases associated with tabular data into the representations. Finally, our qualitative analyses showcase that HyTrel can assimilate the table structure to generate robust representations for the cells, rows, columns, and the entire table.

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IJCAI Conference 2023 Conference Paper

Learning Object Consistency and Interaction in Image Generation from Scene Graphs

Yangkang Zhang
Chenye Meng
Zejian Li
Pei Chen
Guang Yang
Changyuan Yang
Lingyun Sun

This paper is concerned with synthesizing images conditioned on a scene graph (SG), a set of object nodes and their edges of interactive relations. We divide existing works into image-oriented and code-oriented methods. In our analysis, the image-oriented methods do not consider object interaction in spatial hidden feature. On the other hand, in empirical study, the code-oriented methods lose object consistency as their generated images miss certain objects in the input scene graph. To alleviate these two issues, we propose Learning Object Consistency and Interaction (LOCI). To preserve object consistency, we design a consistency module with a weighted augmentation strategy for objects easy to be ignored and a matching loss between scene graphs and image codes. To learn object interaction, we design an interaction module consisting of three kinds of message propagation between the input scene graph and the learned image code. Experiments on COCO-stuff and Visual Genome datasets show our proposed method alleviates the ignorance of objects and outperforms the state-of-the-art on visual fidelity of generated images and objects.

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IS Journal 2018 Journal Article

Multivariate Correlation Entropy and Law Discovery in Large Data Sets

Jianji Wang
Nanning Zheng
Badong Chen
Pei Chen
Shitao Chen
Ziyi Liu
Fei-Yue Wang
Bao Xi

Over the past several centuries, many important natural laws have been discovered by scientists, which have not only changed our viewpoints about nature but also affected our lives significantly. Today, automatic discovery of meaningful laws from data beyond two variables becomes an important task of our time. Here, we propose two multivariate correlation measures, namely, the multivariate correlation entropy (MCE) and the multivariate incorrelation entropy (MIE), which can be used to measure the strength of the correlation among multiple variables. Using MIE makes it possible to directly detect linear relations existing in large data sets. In addition, more complicated nonlinear multivariate laws can be discovered using a function dictionary.

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