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Zhihong Chen

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11 papers

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EAAI Journal 2026 Journal Article

A novel grey model based on fractional derivative and self-adaptive reverse accumulation and its application in energy forecasting

Qiong Wang
Zhihong Chen
Guan Wang
Wei Chen

In response to the global energy crisis and climate change, developing prediction models with high data adaptability is crucial for sustainable development. To address the challenges of general models’ inadequate adaptation to non-smooth, nonlinear energy data and the lack of data memory effects, therefore, a novel grey prediction model based on Caputo fractional derivative is established, effectively enhancing the adaptability of the model by incorporating both the Caputo fractional derivative and a fractional self-adaptive reverse accumulation operator, enabling dynamic memory and the adaptive adjustment of data weight. Additionally, adding a nonlinear correction term and optimizing the background value in the model enhances the performance to fit nonlinear data and further increases prediction accuracy. In this paper, the Laplace transform is employed to derive the analytical solution of the model, while the particle swarm optimization algorithm is utilized to optimize the parameters, ensuring the model achieves optimal performance. To verify the model’s validity, empirical analysis with various energy production and consumption data shows that the model significantly outperforms comparison models, presenting the excellent applicability of data in different types. Finally, the new model is applied to forecast the development trends of the average daily consumption of energy, natural gas, and electricity. The prediction results not only provide practical value for the application in energy forecasting but also offer a reliable theoretical basis and data support for relevant decision-making.

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TMLR Journal 2026 Journal Article

RLHF in an SFT Way: From Optimal Solution to Reward-Weighted Alignment

Yuhao Du
Zhuo Li
Pengyu Cheng
Zhihong Chen
Yuejiao XIE
Xiang Wan
Anningzhe Gao

Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning Large Language Models (LLMs) with human values. However, RLHF has been continuously challenged by its high complexity in implementation and computation consumption, specifically for online sampling-based methods like Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO). Even with recent simplifications, such as Direct Preference Optimization (DPO) that designs an offline implicit reward learning objective relying on pre-collected preference datasets, the problems of over-fitting and training instability remain hindering the alignment process from the expected optimal performance. To address the existing challenges, we propose a novel simplification of RLHF from the perspective of variational inference, called **V**ariational **A**lignment with **R**e-weighting (**VAR**). Specifically, by directly minimizing the distribution gap between the learning LLM policy and the optimal solution of RLHF, we transform the alignment objective into an offline reward-driven re-weighted supervised fine-tuning (SFT) form, which only requires minor adjustment on the SFT loss to obtain noticeable improvement on training stability and effectiveness. In comprehensive evaluation benchmarks, our objective empowers LLMs to outperform offline alignments, demonstrating superior performance in both helpfulness and harmlessness metrics (avg. $\uparrow7.16\%$ than DPO). Meanwhile, when compared to online sampling methods, our method is also comparable even better while significantly reducing computational overhead and accelerating convergence speed (over $5\times$ faster than GRPO), suggesting our approach as an efficient and effective solution in bridging the gap between efficiency and performance in LLM alignment.

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NeurIPS Conference 2024 Conference Paper

RaVL: Discovering and Mitigating Spurious Correlations in Fine-Tuned Vision-Language Models

Maya Varma
Jean-Benoit Delbrouck
Zhihong Chen
Akshay Chaudhari
Curtis Langlotz

Fine-tuned vision-language models (VLMs) often capture spurious correlations between image features and textual attributes, resulting in degraded zero-shot performance at test time. Existing approaches for addressing spurious correlations (i) primarily operate at the global image-level rather than intervening directly on fine-grained image features and (ii) are predominantly designed for unimodal settings. In this work, we present RaVL, which takes a fine-grained perspective on VLM robustness by discovering and mitigating spurious correlations using local image features rather than operating at the global image level. Given a fine-tuned VLM, RaVL first discovers spurious correlations by leveraging a region-level clustering approach to identify precise image features contributing to zero-shot classification errors. Then, RaVL mitigates the identified spurious correlation with a novel region-aware loss function that enables the VLM to focus on relevant regions and ignore spurious relationships during fine-tuning. We evaluate RaVL on 654 VLMs with various model architectures, data domains, and learned spurious correlations. Our results show that RaVL accurately discovers (191% improvement over the closest baseline) and mitigates (8. 2% improvement on worst-group image classification accuracy) spurious correlations. Qualitative evaluations on general-domain and medical-domain VLMs confirm our findings.

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NeurIPS Conference 2024 Conference Paper

WhodunitBench: Evaluating Large Multimodal Agents via Murder Mystery Games

Junlin Xie
Ruifei Zhang
Zhihong Chen
Xiang Wan
Guanbin Li

Recently, large language models (LLMs) have achieved superior performance, empowering the development of large multimodal agents (LMAs). An LMA is anticipated to execute practical tasks requires various capabilities including multimodal perception, interaction, reasoning, and decision making. However, existing benchmarks are limited in assessing compositional skills and actions demanded by practical scenarios, where they primarily focused on single tasks and static scenarios. To bridge this gap, we introduce WhodunitBench, a benchmark rooted from murder mystery games, where players are required to utilize the aforementioned skills to achieve their objective (i. e. , identifying the `murderer' or hiding themselves), providing a simulated dynamic environment for evaluating LMAs. Specifically, WhodunitBench includes two evaluation modes. The first mode, the arena-style evaluation, is constructed from 50 meticulously curated scripts featuring clear reasoning clues and distinct murderers; The second mode, the chain of evaluation, consists of over 3000 curated multiple-choice questions and open-ended questions, aiming to assess every facet of the murder mystery games for LMAs. Experiments show that although current LMAs show acceptable performance in basic perceptual tasks, they are insufficiently equipped for complex multi-agent collaboration and multi-step reasoning tasks. Furthermore, the full application of the theory of mind to complete games in a manner akin to human behavior remains a significant challenge. We hope this work can illuminate the path forward, providing a solid foundation for the future development of LMAs. Our WhodunitBench is open-source and accessible at: https: //github. com/jun0wanan/WhodunitBench-Murder Mystery Games

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AAAI Conference 2023 Conference Paper

A Simple Yet Effective Subsequence-Enhanced Approach for Cross-Domain NER

Jinpeng Hu
DanDan Guo
Yang Liu
Zhuo Li
Zhihong Chen
Xiang Wan
Tsung-Hui Chang

Cross-domain named entity recognition (NER), aiming to address the limitation of labeled resources in the target domain, is a challenging yet important task. Most existing studies alleviate the data discrepancy across different domains at the coarse level via combing NER with language modelings or introducing domain-adaptive pre-training (DAPT). Notably, source and target domains tend to share more fine-grained local information within denser subsequences than global information within the whole sequence, such that subsequence features are easier to transfer, which has not been explored well. Besides, compared to token-level representation, subsequence-level information can help the model distinguish different meanings of the same word in different domains. In this paper, we propose to incorporate subsequence-level features for promoting the cross-domain NER. In detail, we first utilize a pre-trained encoder to extract the global information. Then, we re-express each sentence as a group of subsequences and propose a novel bidirectional memory recurrent unit (BMRU) to capture features from the subsequences. Finally, an adaptive coupling unit (ACU) is proposed to combine global information and subsequence features for predicting entity labels. Experimental results on several benchmark datasets illustrate the effectiveness of our model, which achieves considerable improvements.

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AAAI Conference 2023 Conference Paper

EASAL: Entity-Aware Subsequence-Based Active Learning for Named Entity Recognition

Yang Liu
Jinpeng Hu
Zhihong Chen
Xiang Wan
Tsung-Hui Chang

Active learning is a critical technique for reducing labelling load by selecting the most informative data. Most previous works applied active learning on Named Entity Recognition (token-level task) similar to the text classification (sentence-level task). They failed to consider the heterogeneity of uncertainty within each sentence and required access to the entire sentence for the annotator when labelling. To overcome the mentioned limitations, in this paper, we allow the active learning algorithm to query subsequences within sentences and propose an Entity-Aware Subsequences-based Active Learning (EASAL) that utilizes an effective Head-Tail pointer to query one entity-aware subsequence for each sentence based on BERT. For other tokens outside this subsequence, we randomly select 30% of these tokens to be pseudo-labelled for training together where the model directly predicts their pseudo-labels. Experimental results on both news and biomedical datasets demonstrate the effectiveness of our proposed method. The code is released at https://github.com/lylylylylyly/EASAL.

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JBHI Journal 2022 Journal Article

Attention-Guided Discriminative Region Localization and Label Distribution Learning for Bone Age Assessment

Chao Chen
Zhihong Chen
Xinyu Jin
Lanjuan Li
William Speier
Corey W. Arnold

Bone age assessment (BAA) is clinically important as it can be used to diagnose endocrine and metabolic disorders during child development. Existing deep learning based methods for classifying bone age use the global image as input, or exploit local information by annotating extra bounding boxes or key points. However, training with the global image underutilizes discriminative local information, while providing extra annotations is expensive and subjective. In this paper, we propose an attention-guided approach to automatically localize the discriminative regions for BAA without any extra annotations. Specifically, we first train a classification model to learn the attention maps of the discriminative regions, finding the hand region, the most discriminative region (the carpal bones), and the next most discriminative region (the metacarpal bones). Guided by those attention maps, we then crop the informative local regions from the original image and aggregate different regions for BAA. Instead of taking BAA as a general regression task, which is suboptimal due to the label ambiguity problem in the age label space, we propose using joint age distribution learning and expectation regression, which makes use of the ordinal relationship among hand images with different individual ages and leads to more robust age estimation. Extensive experiments are conducted on the RSNA pediatric bone age data set. Without using extra manual annotations, our method achieves competitive results compared with existing state-of-the-art deep learning-based methods that require manual annotation. Code is available at https://github.com/chenchao666/Bone-Age-Assessment.

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AAAI Conference 2021 Conference Paper

Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing

Zhihong Chen
Taiping Yao
Kekai Sheng
Shouhong Ding
Ying Tai
Jilin Li
Feiyue Huang
Xinyu Jin

Face anti-spoofing approach based on domain generalization (DG) has drawn growing attention due to its robustness for unseen scenarios. Existing DG methods assume that the domain label is known. However, in real-world applications, the collected dataset always contains mixture domains, where the domain label is unknown. In this case, most of existing methods may not work. Further, even if we can obtain the domain label as existing methods, we think this is just a sub-optimal partition. To overcome the limitation, we propose domain dynamic adjustment meta-learning (D2 AM) without using domain labels, which iteratively divides mixture domains via discriminative domain representation and trains a generalizable face anti-spoofing with meta-learning. Specifically, we design a domain feature based on Instance Normalization (IN) and propose a domain representation learning module (DRLM) to extract discriminative domain features for clustering. Moreover, to reduce the side effect of outliers on clustering performance, we additionally utilize maximum mean discrepancy (MMD) to align the distribution of sample features to a prior distribution, which improves the reliability of clustering. Extensive experiments show that the proposed method outperforms conventional DG-based face anti-spoofing methods, including those utilizing domain labels. Furthermore, we enhance the interpretability through visualization.

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AAAI Conference 2020 Conference Paper

HoMM: Higher-Order Moment Matching for Unsupervised Domain Adaptation

Chao Chen
Zhihang Fu
Zhihong Chen
Sheng Jin
Zhaowei Cheng
Xinyu Jin
Xian-Sheng Hua

Minimizing the discrepancy of feature distributions between different domains is one of the most promising directions in unsupervised domain adaptation. From the perspective of moment matching, most existing discrepancy-based methods are designed to match the second-order or lower moments, which however, have limited expression of statistical characteristic for non-Gaussian distributions. In this work, we propose a Higher-order Moment Matching (HoMM) method, and further extend the HoMM into reproducing kernel Hilbert spaces (RKHS). In particular, our proposed HoMM can perform arbitrary-order moment matching, we show that the ﬁrstorder HoMM is equivalent to Maximum Mean Discrepancy (MMD) and the second-order HoMM is equivalent to Correlation Alignment (CORAL). Moreover, HoMM (order≥ 3) is expected to perform ﬁne-grained domain alignment as higher-order statistics can approximate more complex, non- Gaussian distributions. Besides, we also exploit the pseudolabeled target samples to learn discriminative representations in the target domain, which further improves the transfer performance. Extensive experiments are conducted, showing that our proposed HoMM consistently outperforms the existing moment matching methods by a large margin. Codes are available at https: //github. com/chenchao666/HoMM-Master

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AAAI Conference 2019 Conference Paper

Hierarchical Attention Network for Image Captioning

Weixuan Wang
Zhihong Chen
Haifeng Hu

Recently, attention mechanism has been successfully applied in image captioning, but the existing attention methods are only established on low-level spatial features or high-level text features, which limits richness of captions. In this paper, we propose a Hierarchical Attention Network (HAN) that enables attention to be calculated on pyramidal hierarchy of features synchronously. The pyramidal hierarchy consists of features on diverse semantic levels, which allows predicting different words according to different features. On the other hand, due to the different modalities of features, a Multivariate Residual Module (MRM) is proposed to learn the joint representations from features. The MRM is able to model projections and extract relevant relations among different features. Furthermore, we introduce a context gate to balance the contribution of different features. Compared with the existing methods, our approach applies hierarchical features and exploits several multimodal integration strategies, which can significantly improve the performance. The HAN is verified on benchmark MSCOCO dataset, and the experimental results indicate that our model outperforms the state-of-the-art methods, achieving a BLEU1 score of 80. 9 and a CIDEr score of 121. 7 in the Karpathy’s test split.

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AAAI Conference 2019 Conference Paper

Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation

Chao Chen
Zhihong Chen
Boyuan Jiang
Xinyu Jin

Recently, considerable effort has been devoted to deep domain adaptation in computer vision and machine learning communities. However, most of existing work only concentrates on learning shared feature representation by minimizing the distribution discrepancy across different domains. Due to the fact that all the domain alignment approaches can only reduce, but not remove the domain shift, target domain samples distributed near the edge of the clusters, or far from their corresponding class centers are easily to be misclassified by the hyperplane learned from the source domain. To alleviate this issue, we propose to joint domain alignment and discriminative feature learning, which could benefit both domain alignment and final classification. Specifically, an instance-based discriminative feature learning method and a center-based discriminative feature learning method are proposed, both of which guarantee the domain invariant features with better intra-class compactness and inter-class separability. Extensive experiments show that learning the discriminative features in the shared feature space can significantly boost the performance of deep domain adaptation methods.

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