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Huan Chen

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11 papers
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11

AAAI Conference 2026 Conference Paper

HyperCOD: The First Challenging Benchmark and Baseline for Hyperspectral Camouflaged Object Detection

  • Shuyan Bai
  • Tingfa Xu
  • Peifu Liu
  • Yuhao Qiu
  • Huiyan Bai
  • Huan Chen
  • Yanyan Peng
  • Jianan Li

RGB-based camouflaged object detection struggles in real-world scenarios where color and texture cues are ambiguous. While hyperspectral image offers a powerful alternative by capturing fine-grained spectral signatures, progress in hyperspectral camouflaged object detection (HCOD) has been critically hampered by the absence of a dedicated, large-scale benchmark. To spur innovation, we introduce HyperCOD, the first challenging benchmark for HCOD. Comprising 350 high-resolution hyperspectral images, It features complex real-world scenarios with minimal objects, intricate shapes, severe occlusions, and dynamic lighting to challenge current models.The advent of foundation models like the Segment Anything Model (SAM) presents a compelling opportunity. To adapt the Segment Anything Model (SAM) for HCOD, we propose HyperSpectral Camouflage-aware SAM (HSC-SAM). HSC-SAM ingeniously reformulates the hyperspectral image by decoupling it into a spatial map fed to SAM's image encoder and a spectral saliency map that serves as an adaptive prompt. This translation effectively bridges the modality gap. Extensive experiments show that HSC-SAM sets a new state-of-the-art on HyperCOD and generalizes robustly to other public HSI datasets. The HyperCOD dataset and our HSC-SAM baseline provide a robust foundation to foster future research in this emerging area.

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AAAI Conference 2026 Conference Paper

Multi-Aspect Cross-modal Quantization for Generative Recommendation

  • Fuwei Zhang
  • Xiaoyu Liu
  • Dongbo Xi
  • Jishen Yin
  • Huan Chen
  • Peng Yan
  • Fuzhen Zhuang
  • Zhao Zhang

Generative Recommendation (GR) has emerged as a new paradigm in recommender systems. This approach relies on quantized representations to discretize item features, modeling users’ historical interactions as sequences of discrete tokens. Based on these tokenized sequences, GR predicts the next item by employing next-token prediction methods. The challenges of GR lie in constructing high-quality semantic identifiers (IDs) that are hierarchically organized, minimally conflicting, and conducive to effective generative model training. However, current approaches remain limited in their ability to harness multimodal information and to capture the deep and intricate interactions among diverse modalities, both of which are essential for learning high-quality semantic IDs and for effectively training GR models. To address this, we propose Multi-Aspect Cross-modal quantization for generative Recommendation (MACRec), which introduces multimodal information and incorporates it into both semantic ID learning and generative model training from different aspects. Specifically, we first introduce cross-modal quantization during the ID learning process, which effectively reduces conflict rates and thus improves codebook usability through the complementary integration of multimodal information. In addition, to further enhance the generative ability of our GR model, we incorporate multi-aspect cross-modal alignments, including the implicit and explicit alignments. Finally, we conduct extensive experiments on three well-known recommendation datasets to demonstrate the effectiveness of our proposed method.

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AAAI Conference 2026 Conference Paper

Partial Fairness Awareness: Belief-Guided Strategic Mechanism for Strategic Agents

  • Xinpeng Lv
  • Chunyuan Zheng
  • Yunxin Mao
  • Renzhe Xu
  • Hao Zou
  • Shanzhi Gu
  • Liyang Xu
  • Huan Chen

Strategic machine learning investigates scenarios where agents manipulate their features to receive favorable decisions from predictive models. To address fairness concerns intrinsic to strategic classification, recent work has introduced group-specific fairness constraints. However, current fairness-aware approaches face a fundamental dilemma in the issue of fairness exposure: making these constraints public enables strategic manipulation and can lead to fairness reversal, while keeping them hidden may reduce social welfare and discourage genuine improvement. To fill this gap, we subsequently propose the problem of Partial Fairness Awareness (PFA), as our theoretical analysis informs that such a dilemma can be mitigated by releasing the candidate set of fairness constraints and concealing the grounding constraint. To be specific, we introduce a belief-guided strategic mechanism wherein agents iteratively interact with the decision system and maintain a belief distribution over the candidate set of fairness constraints. This belief-guided process enables agents, through iterative interaction and feedback, to update their belief distribution over the candidate set, thereby gradually aligning their belief with the grounding fairness constraint employed by the system. Extensive experiments on real-world and synthetic datasets demonstrate that PFA achieves lower group fairness gaps, higher acceptance of truly qualified individuals, and more stable outcomes compared to fully public or private fairness regimes.

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NeurIPS Conference 2025 Conference Paper

Breaking the Gradient Barrier: Unveiling Large Language Models for Strategic Classification

  • Xinpeng Lv
  • Yunxin Mao
  • Haoxuan Li
  • Ke Liang
  • Jinxuan Yang
  • Wanrong Huang
  • Haoang Chi
  • Huan Chen

Strategic classification (SC) explores how individuals or entities modify their features strategically to achieve favorable classification outcomes. However, existing SC methods, which are largely based on linear models or shallow neural networks, face significant limitations in terms of scalability and capacity when applied to real-world datasets with significantly increasing scale, especially in financial services and the internet sector. In this paper, we investigate how to leverage large language models to design a more scalable and efficient SC framework, especially in the case of growing individuals engaged with decision-making processes. Specifically, we introduce GLIM, a gradient-free SC method grounded in in-context learning. During the feed-forward process of self-attention, GLIM implicitly simulates the typical bi-level optimization process of SC, including both the feature manipulation and decision rule optimization. Without fine-tuning the LLMs, our proposed GLIM enjoys the advantage of cost-effective adaptation in dynamic strategic environments. Theoretically, we prove GLIM can support pre-trained LLMs to adapt to a broad range of strategic manipulations. We validate our approach through experiments with a collection of pre-trained LLMs on real-world and synthetic datasets in financial and internet domains, demonstrating that our GLIM exhibits both robustness and efficiency, and offering an effective solution for large-scale SC tasks.

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UAI Conference 2025 Conference Paper

Enhancing Uncertainty Quantification in Large Language Models through Semantic Graph Density

  • Zhaoye Li
  • Siyuan Shen
  • Wenjing Yang 0002
  • Ruochun Jin
  • Huan Chen
  • Ligong Cao
  • Jing Ren

Large Language Models (LLMs) excel in language understanding but are susceptible to "confabulation, " where they generate arbitrary, factually incorrect responses to uncertain questions. Detecting confabulation in question answering often relies on Uncertainty Quantification (UQ), which measures semantic entropy or consistency among sampled answers. While several methods have been proposed for UQ in LLMs, they suffer from key limitations, such as overlooking fine-grained semantic relationships among answers and neglecting answer probabilities. To address these issues, we propose Semantic Graph Density (SGD). SGD quantifies semantic consistency by evaluating the density of a semantic graph that captures fine-grained semantic relationships among answers. Additionally, it integrates answer probabilities to adjust the contribution of each edge to the overall uncertainty score. We theoretically prove that SGD generalizes the previous state-of-the-art method, Deg, and empirically demonstrate its superior performance across four LLMs and four free-form question-answering datasets. In particular, in experiments with Llama3. 1-8B, SGD outperformed the best baseline by 1. 52% in AUROC on the CoQA dataset and by 1. 22% in AUARC on the TriviaQA dataset.

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PRL Workshop 2025 Workshop Paper

RELAX: Reinforcement Learning Enabled 2D-LiDAR based Autonomous System for Parsimonious UAVs

  • Guanlin Wu
  • Zhuokai Zhao
  • Huan Chen
  • Jinyi Zhao
  • Yangke Zhang
  • Yutao He

Unmanned Aerial Vehicles (UAVs) have become increasingly prominence in recent years, finding applications in surveillance, package delivery, among many others. Despite considerable efforts in developing algorithms that enable UAVs to navigate through complex unknown environments autonomously, they often require expensive hardware and sensors, such as RGB-D cameras and 3D-LiDAR, leading to a persistent trade-off between performance and cost. To this end, we propose RELAX, a novel end-to-end autonomous framework that is exceptionally cost-efficient, requiring only a single 2D-LiDAR to enable UAVs operating in unknown environments. Specifically, RELAX comprises three components: a pre-processing map constructor; an offline mission planner; and a reinforcement learning (RL)-based online replanner. Simulation experiments demonstrate that RELAX offers more robust dynamic navigation compared to existing algorithms, while only costing a fraction of the others. The code will be made public upon acceptance.

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JBHI Journal 2021 Journal Article

PulseGAN: Learning to Generate Realistic Pulse Waveforms in Remote Photoplethysmography

  • Rencheng Song
  • Huan Chen
  • Juan Cheng
  • Chang Li
  • Yu Liu
  • Xun Chen

Remote photoplethysmography (rPPG) is a non-contact technique for measuring cardiac signals from facial videos. High-quality rPPG pulse signals are urgently demanded in many fields, such as health monitoring and emotion recognition. However, most of the existing rPPG methods can only be used to get average heart rate (HR) values due to the limitation of inaccurate pulse signals. In this paper, a new framework based on generative adversarial network, called PulseGAN, is introduced to generate realistic rPPG pulse signals through denoising the chrominance (CHROM) signals. Considering that the cardiac signal is quasi-periodic and has apparent time-frequency characteristics, the error losses defined in time and spectrum domains are both employed with the adversarial loss to enforce the model generating accurate pulse waveforms as its reference. The proposed framework is tested on three public databases. The results show that the PulseGAN framework can effectively improve the waveform quality, thereby enhancing the accuracy of HR, the interbeat interval (IBI) and the related heart rate variability (HRV) features. The proposed method significantly improves the quality of waveforms compared to the input CHROM signals, with the mean absolute error of AVNN (the average of all normal-to-normal intervals) reduced by 41. 19%, 40. 45%, 41. 63%, and the mean absolute error of SDNN (the standard deviation of all NN intervals) reduced by 37. 53%, 44. 29%, 58. 41%, in the cross-database test on the UBFC-RPPG, PURE, and MAHNOB-HCI databases, respectively. This framework can be easily integrated with other existing rPPG methods to further improve the quality of waveforms, thereby obtaining more reliable IBI features and extending the application scope of rPPG techniques.

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JBHI Journal 2021 Journal Article

Ubiquitous Fall Hazard Identification With Smart Insole

  • Diliang Chen
  • Golnoush Asaeikheybari
  • Huan Chen
  • Wenyao Xu
  • Ming-Chun Huang

Falls are leading causes of nonfatal injuries in workplaces which lead to substantial injury and economic consequences. To help avoid fall injuries, safety managers usually need to inspect working areas routinely. However, it is difficult for a limited number of safety managers to inspect fall hazards instantly especially in large workplaces. To address this problem, a novel fall hazard identification method is proposed in this paper which makes it possible for all workers to report the potential hazards automatically. This method is based on the fact that people use different gaits to get across different floor surfaces. Through analyzing gait patterns, potential fall hazards could be identified automatically. In this research, Smart Insole, an insole shaped wearable system for gait analysis, was applied to measure gait patterns for fall hazard identification. Slips and trips are the focus of this study since they are two main causes of falls in workplaces. Five effective gait features were extracted to train a Support Vector Machine (SVM) model for recognizing slip hazard, trip hazard, and safe floor surfaces. Experiment results showed that fall hazards could be recognized with high accuracy (98. 1%).

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AAAI Conference 2020 Short Paper

Session-Level User Satisfaction Prediction for Customer Service Chatbot in E-Commerce (Student Abstract)

  • Riheng Yao
  • Shuangyong Song
  • Qiudan Li
  • Chao Wang
  • Huan Chen
  • Haiqing Chen
  • Daniel Dajun Zeng

This paper aims to predict user satisfaction for customer service chatbot in session level, which is of great practical significance yet rather untouched. It requires to explore the relationship between questions and answers across different rounds of interactions, and handle user bias. We propose an approach to model multi-round conversations within one session and take user information into account. Experimental results on a dataset from a real-world industrial customer service chatbot Alime demonstrate the good performance of our proposed model.

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AAAI Conference 2019 Conference Paper

Incorporating Semantic Similarity with Geographic Correlation for Query-POI Relevance Learning

  • Ji Zhao
  • Dan Peng
  • Chuhan Wu
  • Huan Chen
  • Meiyu Yu
  • Wanji Zheng
  • Li Ma
  • Hua Chai

Point-of-interest (POI) retrieval that searches for relevant destination locations plays a significant role in on-demand ridehailing services. Existing solutions to POI retrieval mainly retrieve and rank POIs based on their semantic similarity scores. Although intuitive, quantifying the relevance of a Query-POI pair by single-field semantic similarity is subject to inherent limitations. In this paper, we propose a novel Query-POI relevance model for effective POI retrieval for ondemand ride-hailing services. Different from existing relevance models, we capture and represent multi-field and local&global semantic features of a Query-POI pair to measure the semantic similarity. Besides, we observe a hidden correlation between origin-destination locations in ride-hailing scenarios, and propose two location embeddings to characterize the specific correlation. By incorporating the geographic correlation with the semantic similarity, our model achieves better performance in POI ranking. Experimental results on two real-world click-through datasets demonstrate the improvements of our model over state-of-the-art methods.

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