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Ping Chen

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25

AAAI Conference 2026 Conference Paper

HiMo-CLIP: Modeling Semantic Hierarchy and Monotonicity in Vision-Language Alignment

  • Ruijia Wu
  • Ping Chen
  • Fei Shen
  • Shaoan Zhao
  • Qiang Hui
  • Huanlin Gao
  • Ting Lu
  • Zhaoxiang Liu

Contrastive vision-language models like CLIP have achieved impressive results in image-text retrieval by aligning image and text representations in a shared embedding space. However, these models often treat text as flat sequences, limiting their ability to handle complex, compositional, and long-form descriptions. In particular, they fail to capture two essential properties of language: semantic hierarchy, which reflects the multi-level compositional structure of text, and semantic monotonicity, where richer descriptions should result in stronger alignment with visual content. To address these limitations, we propose HiMo-CLIP, a representation-level framework that enhances CLIP-style models without modifying the encoder architecture. HiMo-CLIP introduces two key components: a hierarchical decomposition (HiDe) module that extracts latent semantic components from long-form text via in-batch PCA, enabling flexible, batch-aware alignment across different semantic granularities, and a monotonicity-aware contrastive loss (MoLo) that jointly aligns global and component-level representations, encouraging the model to internalize semantic ordering and alignment strength as a function of textual completeness. These components work together to produce structured, cognitively aligned cross-modal representations. Experiments on multiple image-text retrieval benchmarks show that HiMo-CLIP consistently outperforms strong baselines, particularly under long or compositional descriptions.

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TIST Journal 2025 Journal Article

Horizon Forcing: Improving the Recurrent Forecasting of Chaotic Systems

  • Yong Zhuang
  • Matthew Almeida
  • Wei Ding
  • Shafiqul Islam
  • Zihan Li
  • Ping Chen

Chaotic dynamics are ubiquitous in many real-world systems, ranging from biological and industrial processes to climate dynamics and the spread of viruses. These systems are characterized by high sensitivity to initial conditions, making it challenging to predict their future behavior confidently. In this study, we propose a novel deep-learning framework that addresses this challenge by directly exploiting the long-term compounding of local prediction errors during model training, aiming to extend the time horizon for reliable predictions of chaotic systems. Our approach observes the future trajectories of initial errors at a time horizon, modeling the evolution of the loss to that point through the use of two major components: (1) a recurrent architecture (Error Trajectory Tracing) designed to trace the trajectories of predictive errors through phase space, and (2) a training regime, Horizon Forcing, that pushes the model’s focus out to a predetermined time horizon. We validate our method on three classic chaotic systems and six real-world time series prediction tasks with chaotic characteristics. The results show that our approach outperforms the state-of-the-art methods.

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NeurIPS Conference 2025 Conference Paper

LeMiCa: Lexicographic Minimax Path Caching for Efficient Diffusion-Based Video Generation

  • Huanlin Gao
  • Ping Chen
  • Fuyuan Shi
  • Chao Tan
  • Zhaoxiang Liu
  • Fang Zhao
  • Kai Wang
  • Shiguo Lian

We present LeMiCa, a training-free and efficient acceleration framework for diffusion-based video generation. While existing caching strategies primarily focus on reducing local heuristic errors, they often overlook the accumulation of global errors, leading to noticeable content degradation between accelerated and original videos. To address this issue, we formulate cache scheduling as a directed graph with error-weighted edges and introduce a Lexicographic Minimax Path Optimization strategy that explicitly bounds the worst-case path error. This approach substantially improves the consistency of global content and style across generated frames. Extensive experiments on multiple text-to-video benchmarks demonstrate that LeMiCa delivers dual improvements in both inference speed and generation quality. Notably, our method achieves a 2. 9× speedup on the Latte model and reaches an LPIPS score of 0. 05 on Open-Sora, outperforming prior caching techniques. Importantly, these gains come with minimal perceptual quality degradation, making LeMiCa a robust and generalizable paradigm for accelerating diffusion-based video generation. We believe this approach can serve as a strong foundation for future research on efficient and reliable video synthesis.

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AAAI Conference 2025 Conference Paper

Unleashing the Potential of Model Bias for Generalized Category Discovery

  • Wenbin An
  • Haonan Lin
  • Jiahao Nie
  • Feng Tian
  • Wenkai Shi
  • Yaqiang Wu
  • Qianying Wang
  • Ping Chen

Generalized Category Discovery is a significant and complex task that aims to identify both known and undefined novel categories from a set of unlabeled data, leveraging another labeled dataset containing only known categories. The primary challenges stem from model bias induced by pre-training on only known categories and the lack of precise supervision for novel ones, leading to category bias towards known categories and category confusion among different novel categories, which hinders models' ability to identify novel categories effectively. To address these challenges, we propose a novel framework named Self-Debiasing Calibration (SDC). Unlike prior methods that regard model bias towards known categories as an obstacle to novel category identification, SDC provides a novel insight into unleashing the potential of the bias to facilitate novel category learning. Specifically, we utilize the biased pre-trained model to guide the subsequent learning process on unlabeled data. The output of the biased model serves two key purposes. First, it provides an accurate modeling of category bias, which can be utilized to measure the degree of bias and debias the output of the current training model. Second, it offers valuable insights for distinguishing different novel categories by transferring knowledge between similar categories. Based on these insights, SDC dynamically adjusts the output logits of the current training model using the output of the biased model. This approach produces less biased logits to effectively address the issue of category bias towards known categories, and generates more accurate pseudo labels for unlabeled data, thereby mitigating category confusion for novel categories. Experiments on three benchmark datasets show that SDC outperforms SOTA methods, especially in the identification of novel categories.

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AAAI Conference 2024 Conference Paper

A Unified Knowledge Transfer Network for Generalized Category Discovery

  • Wenkai Shi
  • Wenbin An
  • Feng Tian
  • Yan Chen
  • Yaqiang Wu
  • Qianying Wang
  • Ping Chen

Generalized Category Discovery (GCD) aims to recognize both known and novel categories in an unlabeled dataset by leveraging another labeled dataset with only known categories. Without considering knowledge transfer from known to novel categories, current methods usually perform poorly on novel categories due to the lack of corresponding supervision. To mitigate this issue, we propose a unified Knowledge Transfer Network (KTN), which solves two obstacles to knowledge transfer in GCD. First, the mixture of known and novel categories in unlabeled data makes it difficult to identify transfer candidates (i.e., samples with novel categories). For this, we propose an entropy-based method that leverages knowledge in the pre-trained classifier to differentiate known and novel categories without requiring extra data or parameters. Second, the lack of prior knowledge of novel categories presents challenges in quantifying semantic relationships between categories to decide the transfer weights. For this, we model different categories with prototypes and treat their similarities as transfer weights to measure the semantic similarities between categories. On the basis of two treatments, we transfer knowledge from known to novel categories by conducting pre-adjustment of logits and post-adjustment of labels for transfer candidates based on the transfer weights between different categories. With the weighted adjustment, KTN can generate more accurate pseudo-labels for unlabeled data, which helps to learn more discriminative features and boost model performance on novel categories. Extensive experiments show that our method outperforms state-of-the-art models on all evaluation metrics across multiple benchmark datasets. Furthermore, different from previous clustering-based methods that can only work offline with abundant data, KTN can be deployed online conveniently with faster inference speed. Code and data are available at https://github.com/yibai-shi/KTN.

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AAMAS Conference 2024 Conference Paper

Engaging the Elderly in Exercise with Agents: A Gamified Stationary Bike System for Sarcopenia Management

  • Yang Qiu
  • Ping Chen
  • Huiguo Zhang
  • Bo Huang
  • Di Wang
  • Zhiqi Shen

This paper introduces a portable, gamified exercise system with an embedded agent, specifically designed to aid the elderly in lowerbody workouts using stationary bikes. The system integrates a custom-made Internet of Things (IoT) sensing unit, a gamified application, and an agent-embedded backend platform. By leveraging real-time feedback along with historical user data, the agent actively contributes to exercise safety and adherence by customizing the intensity of workouts and managing break periods. This novel approach aims to make cycling exercise for sarcopenia prevention and intervention more engaging and effective, promoting regular participation and potentially improving health outcomes.

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AAAI Conference 2024 Conference Paper

Transfer and Alignment Network for Generalized Category Discovery

  • Wenbin An
  • Feng Tian
  • Wenkai Shi
  • Yan Chen
  • Yaqiang Wu
  • Qianying Wang
  • Ping Chen

Generalized Category Discovery (GCD) is a crucial real-world task that aims to recognize both known and novel categories from an unlabeled dataset by leveraging another labeled dataset with only known categories. Despite the improved performance on known categories, current methods perform poorly on novel categories. We attribute the poor performance to two reasons: biased knowledge transfer between labeled and unlabeled data and noisy representation learning on the unlabeled data. The former leads to unreliable estimation of learning targets for novel categories and the latter hinders models from learning discriminative features. To mitigate these two issues, we propose a Transfer and Alignment Network (TAN), which incorporates two knowledge transfer mechanisms to calibrate the biased knowledge and two feature alignment mechanisms to learn discriminative features. Specifically, we model different categories with prototypes and transfer the prototypes in labeled data to correct model bias towards known categories. On the one hand, we pull instances with known categories in unlabeled data closer to these prototypes to form more compact clusters and avoid boundary overlap between known and novel categories. On the other hand, we use these prototypes to calibrate noisy prototypes estimated from unlabeled data based on category similarities, which allows for more accurate estimation of prototypes for novel categories that can be used as reliable learning targets later. After knowledge transfer, we further propose two feature alignment mechanisms to acquire both instance- and category-level knowledge from unlabeled data by aligning instance features with both augmented features and the calibrated prototypes, which can boost model performance on both known and novel categories with less noise. Experiments on three benchmark datasets show that our model outperforms SOTA methods, especially on novel categories. Theoretical analysis is provided for an in-depth understanding of our model in general. Our code and data are available at https://github.com/Lackel/TAN.

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AAMAS Conference 2023 Conference Paper

A Teachable Agent to Enhance Elderly's Ikigai

  • Ping Chen
  • Xinjia Yu
  • Su Fang Lim
  • Zhiqi Shen

Ikigai is a Japanese term that is argued to be the most used index of well-being in Japanese studies about the elderly. It is often referred to as ‘purpose in life’ and ‘the sense that life is worth living’. Family, work, and friends are common sources of ikigai. However, as people age, they will likely experience a loss of ikigai. Teachable agents (TAs) have long been used in the education field to help students with ‘learning by teaching the agent’. It has been demonstrated that they may instill a sense of purpose, leading to growth in students’ self-esteem. These benefits of teaching a TA may be experienced by the elderly, thereby improving their ikigai. We present a TA which is designed based on the concept of ikigai with the aim of enhancing the ikigai level of the elderly to help them age more healthily. A user study following the phenomenological approach was conducted, and the results demonstrated the attractiveness and effectiveness of our proposed TA design.

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AAAI Conference 2023 Conference Paper

Generalized Category Discovery with Decoupled Prototypical Network

  • Wenbin An
  • Feng Tian
  • Qinghua Zheng
  • Wei Ding
  • Qianying Wang
  • Ping Chen

Generalized Category Discovery (GCD) aims to recognize both known and novel categories from a set of unlabeled data, based on another dataset labeled with only known categories. Without considering differences between known and novel categories, current methods learn about them in a coupled manner, which can hurt model's generalization and discriminative ability. Furthermore, the coupled training approach prevents these models transferring category-specific knowledge explicitly from labeled data to unlabeled data, which can lose high-level semantic information and impair model performance. To mitigate above limitations, we present a novel model called Decoupled Prototypical Network (DPN). By formulating a bipartite matching problem for category prototypes, DPN can not only decouple known and novel categories to achieve different training targets effectively, but also align known categories in labeled and unlabeled data to transfer category-specific knowledge explicitly and capture high-level semantics. Furthermore, DPN can learn more discriminative features for both known and novel categories through our proposed Semantic-aware Prototypical Learning (SPL). Besides capturing meaningful semantic information, SPL can also alleviate the noise of hard pseudo labels through semantic-weighted soft assignment. Extensive experiments show that DPN outperforms state-of-the-art models by a large margin on all evaluation metrics across multiple benchmark datasets. Code and data are available at https://github.com/Lackel/DPN.

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IS Journal 2023 Journal Article

New User Intent Discovery With Robust Pseudo Label Training and Source Domain Joint Training

  • Wenbin An
  • Feng Tian
  • Ping Chen
  • Qinghua Zheng
  • Wei Ding

Discovering new user intents based on existing intents from constantly incoming unlabeled data is an important task in many intelligent systems deployed in the real world (e. g. , dialogue systems). Since data with new intents are completely unlabeled, most current approaches employ clustering methods to generate pseudo labels to train their models. However, due to intent gaps between existing and new intents, pseudo labels generated by these models are noisy, and prior knowledge from existing intents is not fully utilized. To mitigate these issues, we propose a robust pseudo label training and source domain joint-training network to refine the noisy pseudo labels and make full use of prior knowledge. Experimental results on three intent detection datasets show that our model is more effective and robust than state-of-the-art methods. The code and data are released at https://github.com/Lackel/PTJN.

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IS Journal 2022 Journal Article

Maximizing Fairness in Deep Neural Networks via Mode Connectivity

  • Olga Andreeva
  • Matthew Almeida
  • Wei Ding
  • Scott E. Crouter
  • Ping Chen

With frequent reports of biased outcomes of AI systems, fairness rightfully becomes an active area of current ML research. However, while progress has been made on theoretical analysis and formulation of fairness as constraints on error probabilities, our ability to design and train modern deep learning models that reach the targeted fairness goals in practice is still limited. In this work, we focus on an interesting yet common fairness setting, where multiple samples are collected from each individual, and the goal is to maximally reduce performance disparity among individuals while maintaining overall model performance. To obtain such fair deep learning models, we use mode connectivity combined with multiobjective optimization to select the best model out of an identified feasible set of model weight configurations with similar overall performance but different distributions of performance over individuals. Our method is model-agnostic and effectively bridges fairness theory and practice.

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IS Journal 2020 Journal Article

Recognizing Nested Named Entity Based on the Neural Network Boundary Assembling Model

  • Yanping Chen
  • Yuefei Wu
  • Yongbin Qin
  • Ying Hu
  • Zeyu Wang
  • Ruizhang Huang
  • Xinyu Cheng
  • Ping Chen

The task to recognize named entities is often modeled as a sequence labeling process, which selects a label path whose probability is maximum for an input sentence. Because it makes the assumption that the input sentence has a flattened structure, it often fails to recognize nested named entities. In our previous work, a boundary assembling (BA) model was proposed. It is a cascading framework, which identifies named entity boundaries first, and then assembles them into entity candidates for further assessment. This model is effective to recognize nested named entities, but still suffers from poor performance caused by the sparse feature problem. In this article, the BA model is remodeled with the advancement of neural networks, which enables the model to capture semantic information of a sentence by using word embeddings pretrained in external resources. In our experiments, it shows an impressive improvement on the final performance, outperforming the state of the art more than 17% in F-score.

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AAAI Conference 2018 Conference Paper

A Semantic QA-Based Approach for Text Summarization Evaluation

  • Ping Chen
  • Fei Wu
  • Tong Wang
  • Wei Ding

Many Natural Language Processing and Computational Linguistics applications involve the generation of new texts based on some existing texts, such as summarization, text simplification and machine translation. However, there has been a serious problem haunting these applications for decades, that is, how to automatically and accurately assess quality of these applications. In this paper, we will present some preliminary results on one especially useful and challenging problem in NLP system evaluation – how to pinpoint content differences of two text passages (especially for large passages such as articles and books). Our idea is intuitive and very different from existing approaches. We treat one text passage as a small knowledge base, and ask it a large number of questions to exhaustively identify all content points in it. By comparing the correctly answered questions from two text passages, we will be able to compare their content precisely. The experiment using 2007 DUC summarization corpus clearly shows promising results.

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IS Journal 2017 Journal Article

A Set Space Model for Feature Calculus

  • Yanping Chen
  • Qinghua Zheng
  • Ping Chen

Processing natural language at the sentence level suffers from a sparse-feature problem caused by the limited number of words in a sentence. In this article, a Set Space Model (SSM) is proposed to utilize sentence information, the main idea being that, depending on structural characteristics or functional principles of linguistics, features in a sentence can be grouped into different sets. Feature calculus can then operate on the grouped features and capture structural information using external knowledge. The authors implement this method in a traditional information extraction task, with results showing significant and constant improvement in general information extraction.

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IJCAI Conference 2017 Conference Paper

Predicting the Quality of Short Narratives from Social Media

  • Tong Wang
  • Ping Chen
  • Boyang Li

An important and difficult challenge in building computational models for narratives is the automatic evaluation of narrative quality. Quality evaluation connects narrative understanding and generation as generation systems need to evaluate their own products. To circumvent difficulties in acquiring annotations, we employ upvotes in social media as an approximate measure for story quality. We collected 54, 484 answers from a crowd-powered question-and-answer website, Quora, and then used active learning to build a classifier that labeled 28, 320 answers as stories. To predict the number of upvotes without the use of social network features, we create neural networks that model textual regions and the interdependence among regions, which serve as strong benchmarks for future research. To our best knowledge, this is the first large-scale study for automatic evaluation of narrative quality.

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AAAI Conference 2016 Conference Paper

Text Simplification Using Neural Machine Translation

  • Tong Wang
  • Ping Chen
  • John Rochford
  • Jipeng Qiang

Text simplification (TS) is the technique of reducing the lexical, syntactical complexity of text. Existing automatic TS systems can simplify text only by lexical simplification or by manually defined rules. Neural Machine Translation (NMT) is a recently proposed approach for Machine Translation (MT) that is receiving a lot of research interest. In this paper, we regard original English and simplified English as two languages, and apply a NMT model–Recurrent Neural Network (RNN) encoder-decoder on TS to make the neural network to learn text simplification rules by itself. Then we discuss challenges and strategies about how to apply a NMT model to the task of text simplification.

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IS Journal 2015 Journal Article

A Boundary Assembling Method for Chinese Entity-Mention Recognition

  • Yanping Chen
  • Qinghua Zheng
  • Ping Chen

A boundary assembling (BA) method is presented for Chinese entity-mention recognition. Given a sentence, instead of recognizing entity mentions in a unitary style, the authors' BA method first detects boundaries of entity mentions and then assembles detected boundaries into entity-mention candidates. Each candidate is further assessed by a classifier trained on nonlocal features. This method can make better use of nonlocal features and effectively recognize nested entity mentions. Using the ACE 2005 Chinese corpus, the authors' experimental results show an improvement over state-of-the-art techniques, outperforming existing methods in F-score by 5 percent for entity-mention detection and 4. 23 percent for entity-mention recognition.

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AAAI Conference 2002 Conference Paper

The Yard Allocation Problem

  • Ping Chen
  • and Andrew Lim

The Yard Allocation Problem (YAP) is a real-life resource allocation problem faced by the Port of Singapore Authority (PSA). We first show that YAP is NP- Hard. As the problem is NP-Hard, we propose several heuristics, including Tabu Search methods with short and long term memory, a “Squeaky Wheel” Optimization (SWO) method, and a new hybrid which combines SWO with TS to solve the problem. Extensive experiments show very favorable results for our new hybrid method.

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