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Yulong Chen

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

Quality-aware and Soft Consistency Driven Representation Fusion for Incomplete Multi-view Multi-label Classification

Yadong Liu
Waikeung Wong
Yulong Chen
Jie Wen

Multi-view multi-label classification aims to utilize the rich information contained in multiple views for accurate classification. However, in real-world applications, its performance is often severely constrained by the concurrent missingness of both views and labels. To address this problem, this paper first targets the drawback of representation degradation in traditional feature disentanglement methods caused by strong consistency constraints and proposes a soft consistency constraint. This constraint not only effectively aligns the shared information and maximally avoids the compression of information beneficial to the classification task, but it also enhances the aggregation effect of high-quality representations on other representations. Furthermore, to address the coarse-grained problem of traditional fusion strategies, we designed a quality assessment network that achieves instance-level dynamic weighted fusion in a data-driven manner. Extensive experiments on multiple benchmark datasets demonstrate that our method achieves state-of-the-art performance in both incomplete and complete data scenarios, showcasing its robustness and generality.

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AAAI Conference 2025 Conference Paper

Interacted Object Grounding in Spatio-Temporal Human-Object Interactions

Xiaoyang Liu
Boran Wen
Xinpeng Liu
Zizheng Zhou
Hongwei Fan
Cewu Lu
Lizhuang Ma
Yulong Chen

Spatio-temporal Human-Object Interaction (ST-HOI) understanding aims at detecting HOIs from videos, which is crucial for activity understanding. However, existing whole-body-object interaction video benchmarks overlook the truth that open-world objects are diverse, that is, they usually provide limited and predefined object classes. Therefore, we introduce a new open-world benchmark: Grounding Interacted Objects (GIO) including 1,098 interacted objects class and 290K interacted object boxes annotation. Accordingly, an object grounding task is proposed expecting vision systems to discover interacted objects. Even though today’s detectors and grounding methods have succeeded greatly, they perform unsatisfactorily in localizing diverse and rare objects in GIO. This profoundly reveals the limitations of current vision systems and poses a great challenge. Thus, we explore leveraging spatio-temporal cues to address object grounding and propose a 4D question-answering framework (4D-QA) to discover interacted objects from diverse videos. Our method demonstrates significant superiority in extensive experiments compared to current baselines.

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ICML Conference 2025 Conference Paper

Learning Compact Semantic Information for Incomplete Multi-View Missing Multi-Label Classification

Jie Wen 0001
Yadong Liu
Zhanyan Tang
Yuting He 0001
Yulong Chen
Mu Li 0005
Chengliang Liu 0003

Multi-view data involves various data forms, such as multi-feature, multi-sequence and multimodal data, providing rich semantic information for downstream tasks. The inherent challenge of incomplete multi-view missing multi-label learning lies in how to effectively utilize limited supervision and insufficient data to learn discriminative representation. Starting from the sufficiency of multi-view shared information for downstream tasks, we argue that the existing contrastive learning paradigms on missing multi-view data show limited consistency representation learning ability, leading to the bottleneck in extracting multi-view shared information. In response, we propose to minimize task-independent redundant information by pursuing the maximization of cross-view mutual information. Additionally, to alleviate the hindrance caused by missing labels, we develop a dual-branch soft pseudo-label cross-imputation strategy to improve classification performance. Extensive experiments on multiple benchmarks validate our advantages and demonstrate strong compatibility with both missing and complete data.

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JAIR Journal 2024 Journal Article

Cross-domain Constituency Parsing by Leveraging Heterogeneous Data

Peiming Guo
Meishan Zhang
Yulong Chen
Jianling Li
Min Zhang
Yue Zhang

Knowledge transfer is investigated in various natural language processing tasks except cross-domain constituency parsing. In this paper, we leverage heterogeneous data to transfer cross-domain and cross-task knowledge to constituency parsing. Concretely, we first select language modeling, named entity recognition, CCG supertagging and dependency parsing as auxiliary tasks and collect the corpora of these tasks covering various domains as cross-domain and cross-task heterogeneous data. Second, we exploit three types of prefixes: shared, task and domain prefix, to merge cross-domain and cross-task data and decompose the general, task and domain representation in the pretrained language model. Third, we convert the data formats of multi-source heterogeneous datasets and loss objectives of the auxiliary tasks into a consistent formalization closer to constituency parsing. Finally, we jointly train the model to transfer task and domain knowledge to cross-domain constituency parsing. We verify the effectiveness of our proposed model on five target domains of MCTB. Experimental results show that our knowledge transfer model outperforms various baseline models, including conventional chart-based and transition-based parsers and the current large-scale language model for zero-shot and few-shot settings.

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JBHI Journal 2022 Journal Article

ULECGNet: An Ultra-Lightweight End-to-End ECG Classification Neural Network

Jianbiao Xiao
Jiahao Liu
Huanqi Yang
Qingsong Liu
Ning Wang
Zhen Zhu
Yulong Chen
Yu Long

ECG classification is a key technology in intelligent electrocardiogram (ECG) monitoring. In the past, traditional machine learning methods such as support vector machine (SVM) and K-nearest neighbor (KNN) have been used for ECG classification, but with limited classification accuracy. Recently, the end-to-end neural network has been used for ECG classification and shows high classification accuracy. However, the end-to-end neural network has large computational complexity including a large number of parameters and operations. Although dedicated hardware such as field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) can be developed to accelerate the neural network, they result in large power consumption, large design cost, or limited flexibility. In this work, we have proposed an ultra-lightweight end-to-end ECG classification neural network that has extremely low computational complexity (∼8. 2k parameters & ∼227k multiplication/addition operations) and can be squeezed into a low-cost microcontroller (MCU) such as MSP432 while achieving 99. 1% overall classification accuracy. This outperforms the state-of-the-art ECG classification neural network. Implemented on MSP432, the proposed design consumes only 0. 4 mJ and 3. 1 mJ per heartbeat classification for normal and abnormal heartbeats respectively for real-time ECG classification.

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IJCAI Conference 2016 Conference Paper

PARecommender: A Pattern-Based System for Route Recommendation

Feiyi Tang
Jia Zhu
Yang Cao
Sanli Ma
Yulong Chen
Jing He
Changqin Huang
Gansen Zhao

Widely adoption of GPS-enabled devices generates massive trajectory data every minute. The trajectory data can generate meaningful traffic patterns. In this demo, we present a system called PARecommender, which predicts traffic conditions and provides route recommendation based on generated traffic patterns. We first introduce the technical details of PARecommender, and then show several real cases that how PARecommender works.

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