Author name cluster

Qianyu Chen

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

2 author rows

EAAI Journal 2026 Journal Article

A Temporal-spatial Causal Variational Network for accurate sintering temperature forecasting in rotary kilns

Kai Wang
Hua Chen
Xiaogang Zhang
Qianyu Chen
Yuqi Cai
Lei Zhang

Accurate forecasting of sintering temperatures (ST) is pivotal to the high-efficiency, low-energy operation of rotary kilns. The complexity of coupled multivariable process data in industrial environments makes it difficult to uncover patterns and structures in the data, leading to unsatisfactory predictive performance. To accurately analyze temporal–spatial relationships among thermal process variables in rotary kilns, we analyze the causal association among variables and construct a causal graph of the sintering process according to the physicochemical mechanism of sintering. An autoregressive Causal Hidden Markov Model is introduced to model the causal relationships of variables and propagates to generate ST forecasting. In implementation, a generative recurrent neural network, Temporal–spatial Causal Variational Network (TCVN) is designed to generate the representation of hidden variables and extract ST-related features robustly. Each time step in TCVN is composed of a Causal Variational Module (CVM) that integrates a Graph Convolutional Network (GCN) with a Variational Autoencoder (VAE) based on the constructed causal graph. The experiments on real-world data demonstrate that the proposed approach effectively improves the forecasting accuracy of ST with horizons of 1, 3, 6, and 12 steps, confirming the superiority of the proposed model. • A TCVN is proposed for accurate sintering temperature forecasting in rotary kilns. • A causal graph according to the mechanism of sintering is designed. • A CVM is designed to learn the hidden variables in the causal graph. • Detailed experiments are conducted to validate the performance of the TCVN.

Details DOI

AAAI Conference 2025 Conference Paper

CoCoCo: Improving Text-Guided Video Inpainting for Better Consistency, Controllability and Compatibility

Bojia Zi
Shihao Zhao
Xianbiao Qi
Jianan Wang
Yukai Shi
Qianyu Chen
Bin Liang
Rong Xiao

Video inpainting is a crucial task with diverse applications, including fine-grained video editing, video recovery, and video dewatermarking. However, most existing video inpainting methods primarily focus on visual content completion while neglecting text information. There are only a limited number of text-guided video inpainting techniques, and these techniques struggle with maintaining visual quality and exhibit poor semantic representation capabilities. In this paper, we introduce CoCoCo, a text-guided video inpainting diffusion framework. To address the aforementioned challenges, we enhance both the training data and model structure. Specifically, we devise an instance-aware region selection strategy for masked area sampling and develop a novel motion block that incorporates efficient 3D full attention and textual cross attention. Additionally, our CoCoCo framework can be seamlessly integrated with various personalized text-to-image diffusion models through a delicate training-free transfer mechanism. Comprehensive experiments demonstrate that CoCoCo can create high-quality visual content with enhanced temporal consistency, improved text controllability, and better compatibility with personalized image models.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Improving GNN Calibration with Discriminative Ability: Insights and Strategies

Yujie Fang
Xin Li
Qianyu Chen
Mingzhong Wang

The widespread adoption of Graph Neural Networks (GNNs) has led to an increasing focus on their reliability. To address the issue of underconfidence in GNNs, various calibration methods have been developed to gain notable reductions in calibration error. However, we observe that existing approaches generally fail to enhance consistently, and in some cases even deteriorate, GNNs' ability to discriminate between correct and incorrect predictions. In this study, we advocate the significance of discriminative ability and the inclusion of relevant evaluation metrics. Our rationale is twofold: 1) Overlooking discriminative ability can inadvertently compromise the overall quality of the model; 2) Leveraging discriminative ability can significantly inform and improve calibration outcomes. Therefore, we thoroughly explore the reasons why existing calibration methods have ineffectiveness and even degradation regarding the discriminative ability of GNNs. Building upon these insights, we conduct GNN calibration experiments across multiple datasets using a straightforward example model, denoted as DC(GNN). Its excellent performance confirms the potential of integrating discriminative ability as a key consideration in the calibration of GNNs, thereby establishing a pathway toward more effective and reliable network calibration.

PDF Details DOI

ICLR Conference 2024 Conference Paper

Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages

Jinyi Hu
Yuan Yao 0013
Chongyi Wang
Shan Wang 0015
Yinxu Pan
Qianyu Chen
Tianyu Yu 0002
Hanghao Wu

Recently there has been a significant surge in multimodal learning in terms of both image-to-text and text-to-image generation. However, the success is typically limited to English, leaving other languages largely behind. Building a competitive counterpart in other languages is highly challenging due to the low-resource nature of non-English multimodal data (i.e., lack of large-scale, high-quality image-text data). In this work, we propose MPM, an effective training paradigm for training large multimodal models in low-resource languages. MPM demonstrates that Multilingual language models can Pivot zero-shot Multimodal learning across languages. Specifically, based on a strong multilingual large language model, multimodal models pretrained on English-only image-text data can well generalize to other languages in a (quasi)-zero-shot manner, even surpassing models trained on image-text data in native languages. Taking Chinese as a practice of MPM, we build large multimodal models VisCPM in image-to-text and text-to-image generation, which achieve state-of-the-art (open-source) performance in Chinese. To facilitate future research, we open-source codes and model weights at https://github.com/OpenBMB/VisCPM.

Details

AAAI Conference 2023 Conference Paper

Context-Aware Safe Medication Recommendations with Molecular Graph and DDI Graph Embedding

Qianyu Chen
Xin Li
Kunnan Geng
Mingzhong Wang

Molecular structures and Drug-Drug Interactions (DDI) are recognized as important knowledge to guide medication recommendation (MR) tasks, and medical concept embedding has been applied to boost their performance. Though promising performance has been achieved by leveraging Graph Neural Network (GNN) models to encode the molecular structures of medications or/and DDI, we observe that existing models are still defective: 1) to differentiate medications with similar molecules but different functionality; or/and 2) to properly capture the unintended reactions between drugs in the embedding space. To alleviate this limitation, we propose Carmen, a cautiously designed graph embedding-based MR framework. Carmen consists of four components, including patient representation learning, context information extraction, a context-aware GNN, and DDI encoding. Carmen incorporates the visit history into the representation learning of molecular graphs to distinguish molecules with similar topology but dissimilar activity. Its DDI encoding module is specially devised for the non-transitive interaction DDI graphs. The experiments on real-world datasets demonstrate that Carmen achieves remarkable performance improvement over state-of-the-art models and can improve the safety of recommended drugs with a proper DDI graph encoding.

PDF Details DOI