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Yiming Du

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5 papers

1 author row

AAAI Conference 2026 Conference Paper

Deep Incomplete Multi-View Clustering via Hierarchical Imputation and Alignment

Yiming Du
Ziyu Wang
Jian Li
Rui Ning
Lusi Li

Incomplete multi-view clustering (IMVC) aims to discover shared cluster structures from multi-view data with partial observations. The core challenges lie in accurately imputing missing views without introducing bias, while maintaining semantic consistency across views and compactness within clusters. To address these challenges, we propose DIMVC-HIA, a novel deep IMVC framework that integrates hierarchical imputation and alignment with four key components: (1) view-specific autoencoders for latent feature extraction, coupled with a view-shared clustering predictor to produce soft cluster assignments; (2) a hierarchical imputation module that first estimates missing cluster assignments based on cross-view contrastive similarity, and then reconstructs missing features using intra-view, intra-cluster statistics; (3) an energy-based semantic alignment module, which promotes intra-cluster compactness by minimizing energy variance around low-energy cluster anchors; and (4) a contrastive assignment alignment module, which enhances cross-view consistency and encourages confident, well-separated cluster predictions. Experiments on benchmarks demonstrate that our framework achieves superior performance under varying levels of missingness.

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AAAI Conference 2026 Conference Paper

MemGuide: Intent-Driven Memory Selection for Goal-Oriented Multi-Session LLM Agents

Yiming Du
Bingbing Wang
Yang He
Bin Liang
Baojun Wang
Zhongyang Li
Lin Gui
Jeff Z. Pan

Modern task-oriented dialogue (TOD) systems increasingly rely on large language model (LLM) agents, leveraging Retrieval-Augmented Generation (RAG) and long-context capabilities for long-term memory utilization. However, these methods prioritise semantic similarity over task intent, degrading multi-session coherence. We propose MemGuide, a two-stage intent-driven memory selection framework: (1) Intent‑Aligned Retrieval retrieves goal-consistent QA‑formatted memory units; (2) Missing‑Slot Guided Filtering reranks units by slot-completion gain via a chain‑of‑thought reasoner and fine‑tuned LLaMA‑8B filter. We also introduce the MS-TOD, the first multi-session TOD benchmark with 132 diverse personas, 956 task goals, and annotated intent-aligned memory targets. Evaluations on MS-TOD show that MemGuide boosts task success rate by 11% (88%→99%) and reduces dialogue length by 2.84 turns, and matches single‑session performance.

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AAAI Conference 2025 Conference Paper

A New Formula for Sticker Retrieval: Reply with Stickers in Multi-Modal and Multi-Session Conversation

Bingbing Wang
Yiming Du
Bin Liang
Zhixin Bai
Min Yang
Baojun Wang
Kam-Fai Wong
Ruifeng Xu

Stickers are widely used in online chatting, which can vividly express someone's intention, emotion, or attitude. Existing conversation research typically retrieves stickers based on a single session or the previous textual information, which can not adapt to the multi-modal and multi-session nature of the real-world conversation. To this end, we introduce MultiChat, a new dataset for sticker retrieval facing the multi-modal and multi-session conversation, comprising 1,542 sessions, featuring 50,192 utterances and 2,182 stickers. Based on the created dataset, we propose a novel Intent-Guided Sticker Retrieval (IGSR) framework that retrieves stickers for multi-modal and multi-session conversation history drawing support from intent learning. Specifically, we introduce sticker attributes to better leverage the sticker information in multi-modal conversation, which are incorporated with utterances to construct a memory bank. Further, we extract relevant memories for the current conversation from the memory bank to identify the intent of the current conversation, and then retrieve a sticker to respond guided by the intent. Extensive experiments on our MultiChat dataset reveal the robustness and effectiveness of our IGSR approach in multi-session, multi-modal scenarios.

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EAAI Journal 2025 Journal Article

Deep Content and Contrastive Perception learning for automatic fetal nuchal translucency image quality assessment

Lili Zhao
Yuanyuan Xu
Jian Xu
Weiping Ding
Jinzhao Yang
Huiyu Zhou
Yiming Du
Bin Hu

Automatic quality assessment of fetal nuchal translucency ultrasound images can assist physicians in obtaining standard planes and improve the reproducibility of nuchal translucency screening. At present, there are no special studies and methods for the quality assessment of fetal nuchal translucency ultrasound images. For this task, main challenges are low image quality, content identification of structural integrity and relative position relationship, time consumption for data collection and fine-grained annotation. To address these challenges, we propose a framework based on DenseNet model, which includes preprocessing module, content perception module, attention learning module and contrastive regularization module. Experiments show that the modules are effective for improving the quality assessment framework performance. And this framework is better than the other fourteen deep learning models. This framework can provide the sonographer with a model interpretable reference map. Bland–Altman experimental analysis also verifies the consistency between the results obtained by the automatic quality assessment framework and the manually annotated clinical dataset. Therefore, the proposed quality assessment framework for fetal nuchal translucency ultrasound images has the prospect and value of clinical application.

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AIIM Journal 2019 Journal Article

Motor imagery EEG recognition with KNN-based smooth auto-encoder

Xianlun Tang
Ting Wang
Yiming Du
Yuyan Dai

As new human-computer interaction technology, brain-computer interface has been widely used in various fields of life. The study of EEG signals cannot only improve people's awareness of the brain, but also establish new ways for the brain to communicate with the outside world. This paper takes the motion imaging EEG signal as the research object and proposes an innovative semi-supervised model called KNN-based smooth auto-encoder (k-SAE). K-SAE looks for the nearest neighbor values of the samples to construct a new input and learns the robust features representation by reconstructing this new input instead of the original input, which is different from the traditional automatic encoder (AE). The Gaussian filter is selected as the convolution kernel function in k-SAE to smooth the noise in the feature. Besides, the data information and spatial position of the feature map are recorded by max-pooling and unpooling, that help to prevent loss of important information. The method is applied to two data sets for feature extraction and classification experiments of motor imaging EEG signals. The experimental results show that k-SAE achieves good recognition accuracy and outperforms other state-of-the-art recognition algorithms.

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