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Chen Zhao

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31 papers

1 author row

JBHI Journal 2026 Journal Article

FAT-Net: Frequency-Domain Attention-Guided Topology-Refinement Network for Coronary Artery Segmentation in Invasive Coronary Angiography

Nan Mu
Ruiqi Song
Xiaoning Li
Zhihui Xu
Jingfeng Jiang
Chen Zhao

Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide. Although invasive coronary angiography (ICA) is widely used in clinical practice, accurately identifying arterial stenosis is still challenging due to low contrast, heavy noise, and complex vessel morphology. This study introduces the Frequency-Domain Attention-Guided Topology-Refinement Network (FAT-Net) to enhance coronary artery segmentation and stenosis detection in ICA. FAT-Net integrates a frequency-domain Multi-Level Self-Attention (MLSA) mechanism with a cascaded fusion strategy, enabling effective modeling of vascular structures and contextual dependencies across high- and low-frequency components, while improving robustness against background noise. Additionally, the proposed Low-Frequency Decomposition Module (LFDM) performs multi-level wavelet decomposition to progressively denoise ICAs and preserve global vascular topology. High-frequency details are then restored via inverse fusion, continuously refining arterial edges and small branches. Extensive experiments demonstrate that FAT-Net achieves a mean Dice coefficient of 0. 87 for coronary artery segmentation and a True Positive Rate (TPR) of 0. 61 for stenosis detection. The high Dice coefficient indicates accurate vascular segmentation, while the TPR slightly exceeds the levels reported in prior automated stenosis assessment studies, suggesting clinically meaningful detection performance. These results suggests that FAT-Net has strong potential to support accurate CAD diagnosis and treatment planning.

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AAAI Conference 2026 Short Paper

Fine-tuning Zero-shot Large Language Models for Patient-reported Outcomes (Student Abstract)

Yang Yan
Matthew W. Chen
Jiayi Lyu
Chen Zhao
Hao Gao
Zhong Chen

Radiotherapy (RT) is a cornerstone of cancer treatment. Following RT, patient-reported outcomes (PROs) collected via standardized questionnaires are crucial for monitoring patients' quality of life and side effects. However, traditional statistical and machine learning methods, which rely on structured numerical data, often fail to capture semantic meaning within patients' health status. To address this, we developed a novel framework using zero- and few-shot large language models (LLMs) to identify patients experiencing mild to severe depression. Furthermore, classification performance is enhanced through parameter-efficient fine-tuning. Experiments on a prostate cancer PRO dataset for depression have demonstrated that our fine-tuned LLMs consistently outperformed other baseline methods across key evaluation metrics.

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AAAI Conference 2026 Conference Paper

LLM-Enhanced Energy Contrastive Learning for Out-of-Distribution Detection in Text-Attributed Graphs

Xiaoxu Ma
Dong Li
Minglai Shao
Xintao Wu
Chen Zhao

Text-attributed graphs, where nodes are enriched with textual attributes, have become a powerful tool for modeling real-world networks such as citation, social, and transaction networks. However, existing methods for learning from these graphs often assume that the distributions of training and testing data are consistent. This assumption leads to significant performance degradation when faced with out-of-distribution (OOD) data. In this paper, we address the challenge of node-level OOD detection in text-attributed graphs, with the goal of maintaining accurate node classification while simultaneously identifying OOD nodes. We propose a novel approach, LLM-Enhanced Energy Contrastive Learning for Out-of-Distribution Detection in Text-Attributed Graphs (LECT), which integrates large language models (LLMs) and energy-based contrastive learning. The proposed method involves generating high-quality OOD samples by leveraging the semantic understanding and contextual knowledge of LLMs to create dependency-aware pseudo-OOD nodes, and applying contrastive learning based on energy functions to distinguish between in-distribution (IND) and OOD nodes. The effectiveness of our method is demonstrated through extensive experiments on six benchmark datasets, where our method consistently outperforms state-of-the-art baselines, achieving both high classification accuracy and robust OOD detection capabilities.

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AAAI Conference 2026 Conference Paper

MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery

Dong Li
Zhengzhang Chen
Xujiang Zhao
Linlin Yu
Zhong Chen
Yi He
Haifeng Chen
Chen Zhao

Uncovering causal structures from observational data is crucial for understanding complex systems and making informed decisions. While reinforcement learning (RL) has shown promise in identifying these structures in the form of a directed acyclic graph (DAG), existing methods often lack efficiency, making them unsuitable for online applications. In this paper, we propose MARLIN, an efficient multi-agent RL-based approach for incremental DAG learning. MARLIN uses a DAG generation policy that maps a continuous real-valued space to the DAG space as an intra-batch strategy, then incorporates two RL agents—state-specific and state-invariant—to uncover causal relationships and integrates these agents into an incremental learning framework. Furthermore, the framework leverages a factored action space to enhance parallelization efficiency. Extensive experiments on synthetic and real datasets demonstrate that MARLIN outperforms state-of-the-art methods in terms of both efficiency and effectiveness.

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AAAI Conference 2026 Conference Paper

Monte Carlo Diffusion for Generalizable Learning-Based RANSAC

Jiale Wang
Chen Zhao
Wei Ke
Tong Zhang

Random Sample Consensus (RANSAC) is a fundamental approach for robustly estimating parametric models from noisy data. Existing learning-based RANSAC methods utilize deep learning to enhance the robustness of RANSAC against outliers. However, these approaches are trained and tested on the data generated by the same algorithms, leading to limited generalization to out-of-distribution data during inference. Therefore, in this paper, we introduce a novel diffusion-based paradigm that progressively injects noise into ground-truth data, simulating the noisy conditions for training learning-based RANSAC. To enhance data diversity, we incorporate Monte Carlo sampling into the diffusion paradigm, approximating diverse data distributions by introducing different types of randomness at multiple stages. We evaluate our approach in the context of feature matching through comprehensive experiments on the ScanNet and MegaDepth datasets. The experimental results demonstrate that our Monte Carlo diffusion mechanism significantly improves the generalization ability of learning-based RANSAC. We also develop extensive ablation studies that highlight the effectiveness of key components in our framework.

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EAAI Journal 2026 Journal Article

More realistic and accurate precipitation nowcasting with Conditional Rectified Flow Transformers

Yunlong Zhou
Chen Zhao
Fanfan Ji
Renlong Hang
Qingshan Liu
Xiao-Tong Yuan

Precipitation nowcasting plays a critical role in disaster prevention and daily life but remains challenging due to the intricate spatiotemporal dynamics of atmospheric processes. In response to these challenges, recent research has shown that diffusion models can generate visually realistic precipitation results. However, challenges such as accurately predicting precipitation positions and improving inference speed remain unresolved. To address these issues, we propose a novel Conditional Rectified Flow Transformers (CRFT) architecture to improve precipitation nowcasting, which is designed to deliver both predictive accuracy and visual realism. At its core, CRFT features an efficient latent space predictor powered by OmniFormer blocks, which integrate spatial, temporal, and spatiotemporal Transformers to holistically capture the atmosphere dynamics. We explore five variants of spatiotemporal dynamic information interactions for OmniFormer and demonstrate that integrating triple Transformers achieves the best performance. Additionally, we significantly reduce inference time by employing a rectified flow approach, achieving a reduction in inference steps by 98. 4% compared to existing methods, enabling high-quality 20-frame predictions within 2 s. Evaluated on three benchmark datasets, CRFT outperforms state-of-the-art (SOTA) models in both accuracy and quality across multiple metrics, offering an accurate and efficient solution for real-world nowcasting. The code is publicly available at https: //github. com/RuntimeWarning/CRFT.

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AAAI Conference 2026 Conference Paper

Out-of-Distribution Detection with Positive and Negative Prompt Supervision Using Large Language Models

Zhixia He
Chen Zhao
Minglai Shao
Xintao Wu
Xujiang Zhao
Dong Li
Qin Tian
Linlin Yu

Out-of-distribution (OOD) detection is committed to delineating the classification boundaries between in-distribution (ID) and OOD images. Recent advances in vision-language models (VLMs) have demonstrated remarkable OOD detection performance by integrating both visual and textual modalities. In this context, negative prompts are introduced to emphasize the dissimilarity between image features and prompt content. However, these prompts often include a broad range of non-ID features, which may result in suboptimal outcomes due to the capture of overlapping or misleading information. To address this issue, we propose Positive and Negative Prompt Supervision, which encourages negative prompts to capture inter-class features and transfers this semantic knowledge to the visual modality to enhance OOD detection performance. Our method begins with class-specific positive and negative prompts initialized by large language models (LLMs). These prompts are subsequently optimized, with positive prompts focusing on features within each class, while negative prompts highlight features around category boundaries. Additionally, a graph-based architecture is employed to aggregate semantic-aware supervision from the optimized prompt representations and propagate it to the visual branch, thereby enhancing the performance of the energy-based OOD detector. Extensive experiments on two benchmarks, CIFAR-100 and ImageNet-1K, across eight OOD datasets and five different LLMs, demonstrate that our method outperforms state-of-the-art baselines.

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AAAI Conference 2026 Conference Paper

QiMeng-Kernel: Macro-Thinking Micro-Coding Paradigm for LLM-Based High-Performance GPU Kernel Generation

Xinguo Zhu
Shaohui Peng
Jiaming Guo
Yunji Chen
Qi Guo
Yuanbo Wen
Hang Qin
Ruizhi Chen

Developing high-performance GPU kernels is critical for AI and scientific computing, but remains challenging due to its reliance on expert crafting and poor portability. While large language models (LLMs) offer promise for automation, both general-purpose and finetuned LLMs suffer from two fundamental and conflicting limitations: correctness and efficiency. The key reason is that existing LLM-based approaches directly generate the entire optimized low-level programs, requiring exploration of an extremely vast space encompassing both optimization policies and implementation codes. To address the challenge of exploring an intractable space, we propose Macro Thinking Micro Coding (MTMC), a hierarchical framework inspired by the staged optimization strategy of human experts. It decouples optimization strategy from implementation details, ensuring efficiency through high-level strategy and correctness through low-level implementation. Specifically, Macro Thinking employs reinforcement learning to guide lightweight LLMs in efficiently exploring and learning semantic optimization strategies that maximize hardware utilization. Micro Coding leverages general-purpose LLMs to incrementally implement the stepwise optimization proposals from Macro Thinking, avoiding full-kernel generation errors. Together, they effectively navigate the vast optimization space and intricate implementation details, enabling LLMs for high-performance GPU kernel generation. Comprehensive results on widely adopted benchmarks demonstrate the superior performance of MTMC on GPU kernel generation in both accuracy and running time. On KernelBench, MTMC achieves near 100% and 70% accuracy at Levels 1-2 and 3, over 50% than SOTA general-purpose and domain-finetuned LLMs, with up to 7.3× speedup over LLMs, and 2.2× over expert-optimized PyTorch Eager kernels. On the more challenging TritonBench, MTMC attains up to 59.64% accuracy and 34× speedup. All models and datasets will be made publicly available.

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AAAI Conference 2026 Conference Paper

SkillGen: Learning Domain Skills for In-Context Sequential Decision Making

Ruomeng Ding
Wei Cheng
Minglai Shao
Chen Zhao

Large language models (LLMs) are increasingly applied to sequential decision-making through in-context learning (ICL), yet their effectiveness is highly sensitive to prompt quality. Effective prompts should meet three principles: focus on decision-critical information, provide step-level granularity, and minimize reliance on expert annotations through label efficiency. However, existing ICL methods often fail to satisfy all three criteria simultaneously. Motivated by these challenges, we introduce SkillGen, a skill-based ICL framework for structured sequential reasoning. It constructs an action-centric, domain-level graph from sampled trajectories, identifies high-utility actions via temporal-difference credit assignment, and retrieves step-wise skills to generate fine-grained, context-aware prompts. We further present a theoretical analysis showing that focusing on high-utility segments supports task identifiability and informs more effective ICL prompt design. Experiments on ALFWorld, BabyAI, and ScienceWorld, using both open-source and proprietary LLMs, show that SkillGen achieves consistent gains, improving progress rate by 5.9%–16.5% on average across models.

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AAAI Conference 2026 Conference Paper

YOLO-IOD: Towards Real Time Incremental Object Detection

Shizhou Zhang
Xueqiang Lv
Yinghui Xing
Qirui Wu
Di Xu
Chen Zhao
Yanning Zhang

Current methodologies for incremental object detection (IOD) primarily rely on Faster R-CNN or DETR series detectors; however, these approaches do not accommodate the real-time YOLO detection frameworks. In this paper, we first identify three primary types of knowledge conflicts that contribute to catastrophic forgetting in YOLO-based incremental detectors: foreground-background confusion, parameter interference, and misaligned knowledge distillation. Subsequently, we introduce YOLO-IOD, a real-time Incremental Object Detection (IOD) framework that is constructed upon the pretrained YOLO-World model, facilitating incremental learning via a stage-wise parameter-efficient finetuning process. Specifically, YOLO-IOD encompasses three principal components: 1) Conflict-Aware Pseudo-Label Refinement (CPR), which mitigates the foreground-background confusion by leveraging the confidence levels of pseudo labels and identifying potential objects relevant to future tasks. 2) Importance-based Kernel Selection (IKS), which identifies and updates the pivotal convolution kernels pertinent to the current task during the current learning stage. 3)Cross-Stage Asymmetric Knowledge Distillation (CAKD), which addresses the misaligned knowledge distillation conflict by transmitting the features of the student target detector through the detection heads of both the previous and current teacher detectors, thereby facilitating asymmetric distillation between existing and newly introduced categories. We further introduce LoCo COCO, a more realistic benchmark that eliminates data leakage across stages. Experiments on both conventional and LoCo COCO benchmarks show that YOLO-IOD achieves superior performance with minimal forgetting.

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IJCAI Conference 2025 Conference Paper

A Novel Sparse Active Online Learning Framework for Fast and Accurate Streaming Anomaly Detection Over Data Streams

Zhong Chen
Yi He
Di Wu
Chen Zhao
Meikang Qiu

Online Anomaly Detection (OAD) is critical for identifying rare yet important data points in large, dynamic, and complex data streams. A key challenge lies in achieving accurate and consistent detection of anomalies while maintaining computational and memory efficiency. Conventional OAD approaches, which depend on distributional deviations and static thresholds, struggle with model update delays and catastrophic forgetting, leading to missed detections and high false positive rates. To address these limitations, we propose a novel Streaming Anomaly Detection (SAD) method, grounded in a sparse active online learning framework. Our approach uniquely integrates ℓ1, 2-norm sparse online learning with CUR decomposition-based active learning, enabling simultaneous fast feature selection and dynamic instance selection. The efficient CUR decomposition further supports real-time residual analysis for anomaly scoring, eliminating the need for manual threshold settings about temporal data distributions. Extensive experiments on diverse streaming datasets demonstrate SAD's superiority, achieving a 14. 06% reduction in detection error rates compared to five state-of-the-art competitors.

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AAAI Conference 2025 Conference Paper

Exploiting Multimodal Spatial-temporal Patterns for Video Object Tracking

Xiantao Hu
Ying Tai
Xu Zhao
Chen Zhao
Zhenyu Zhang
Jun Li
Bineng Zhong
Jian Yang

Multimodal tracking has garnered widespread attention as a result of its ability to effectively address the inherent limitations of traditional RGB tracking. However, existing multimodal trackers mainly focus on the fusion and enhancement of spatial features or merely leverage the sparse temporal relationships between video frames. These approaches do not fully exploit the temporal correlations in multimodal videos, making it difficult to capture the dynamic changes and motion information of targets in complex scenarios. To alleviate this problem, we propose a unified multimodal spatial-temporal tracking approach named STTrack. In contrast to previous paradigms that solely relied on updating reference information, we introduced a temporal state generator (TSG) that continuously generates a sequence of tokens containing multimodal temporal information. These temporal information tokens are used to guide the localization of the target in the next time state, establish long-range contextual relationships between video frames, and capture the temporal trajectory of the target. Furthermore, at the spatial level, we introduced the mamba fusion and background suppression interactive (BSI) modules. These modules establish a dual-stage mechanism for coordinating information interaction and fusion between modalities. Extensive comparisons on five benchmark datasets illustrate that STTrack achieves state-of-the-art performance across various multimodal tracking scenarios.

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IJCAI Conference 2025 Conference Paper

FADE: Towards Fairness-aware Data Generation for Domain Generalization via Classifier-Guided Score-based Diffusion Models

Yujie Lin
Dong Li
Minglai Shao
Guihong Wan
Chen Zhao

Fairness-aware domain generalization (FairDG) has emerged as a critical challenge for deploying trustworthy AI systems, particularly in scenarios involving distribution shifts. Traditional methods for addressing fairness have failed in domain generalization due to their lack of consideration for distribution shifts. Although disentanglement has been used to tackle FairDG, it is limited by its strong assumptions. To overcome these limitations, we propose Fairness-aware Classifier-Guided Score-based Diffusion Models (FADE) as a novel approach to effectively address the FairDG issue. Specifically, we first pre-train a score-based diffusion model (SDM) and two classifiers to equip the model with strong generalization capabilities across different domains. Then, we guide the SDM using these pre-trained classifiers to effectively eliminate sensitive information from the generated data. Finally, the generated fair data is used to train downstream classifiers, ensuring robust performance under new data distributions. Extensive experiments on three real-world datasets demonstrate that FADE not only enhances fairness but also improves accuracy in the presence of distribution shifts. Additionally, FADE outperforms existing methods in achieving the best accuracy-fairness trade-offs.

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AAAI Conference 2025 Conference Paper

Metric-Agnostic Continual Learning for Sustainable Group Fairness

Heng Lian
Chen Zhao
Zhong Chen
Xingquan Zhu
My T. Thai
Yi He

Group Fairness-aware Continual Learning (GFCL) aims to eradicate discriminatory predictions against certain demographic groups in a sequence of diverse learning tasks. This paper explores an even more challenging GFCL problem – how to sustain a fair classifier across a sequence of tasks with covariate shifts and unlabeled data. We propose the MacFRL solution, with its key idea to optimize the sequence of learning tasks. We hypothesize that high-confident learning can be enabled in the optimized task sequence, where the classifier learns from a set of prioritized tasks to glean knowledge, thereby becoming more capable to handle the tasks with substantial distribution shifts that were originally deferred. Theoretical and empirical studies substantiate that MacFRL excels among its GFCL competitors in terms of prediction accuracy and group fair-ness metrics.

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TMLR Journal 2025 Journal Article

Multi-Modal Foundation Models for Computational Pathology: A Survey

Dong Li
Guihong Wan
Xintao Wu
Xinyu Wu
Xiaohui Chen
Yi He
Zhong Chen
Peter K Sorger

Foundation models have emerged as a powerful paradigm in computational pathology (CPath), enabling scalable and generalizable analysis of histopathological images. While early developments centered on uni-modal models trained solely on visual data, recent advances have highlighted the promise of multi-modal foundation models that integrate heterogeneous data sources such as textual reports, structured domain knowledge, and molecular profiles. In this survey, we provide a comprehensive and up-to-date review of multi-modal foundation models in CPath, with a particular focus on models built upon hematoxylin and eosin (H&E) stained whole slide images (WSIs) and tile-level representations. We categorize 34 state-of-the-art multi-modal foundation models into three major paradigms: vision-language, vision-knowledge graph, and vision-gene expression. We further divide vision-language models into non-LLM-based and LLM-based approaches. Additionally, we analyze 30 available multi-modal datasets tailored for pathology, grouped into image-text pairs, instruction datasets, and image-other modality pairs. Our survey also presents a taxonomy of downstream tasks, highlights training and evaluation strategies, and identifies key challenges and future directions. We aim for this survey to serve as a valuable resource for researchers and practitioners working at the intersection of pathology and AI.

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AAAI Conference 2025 Short Paper

Multi-View Unsupervised Column Subset Selection via Combinatorial Search (Student Abstract)

Guihong Wan
Ninghui Hao
Crystal Maung
Haim Schweitzer
Chen Zhao
Kun-Hsing Yu
Yevgeniy R. Semenov

Given a data matrix, unsupervised column subset selection refers to the problem of identifying a subset of columns that can be used to linearly approximate the original data matrix. This problem has many applications, such as feature selection and representative selection, but solving it optimally is known to be NP-hard. We consider multi-view unsupervised column subset selection, which extends the concept of (single-view) column subset selection to data represented in multiple views or modalities. We introduce a combinatorial search algorithm for this generalized problem. One variant of the algorithm is guaranteed to compute an optimal solution in a setting similar to the classical A* algorithm. Other suboptimal variants, in a setting similar to the weighted A* algorithm, are much faster and provide a solution along with a bound on its quality.

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IJCAI Conference 2025 Conference Paper

QiMeng-TensorOp: One-Line Prompt is Enough for High-Performance Tensor Operator Generation with Hardware Primitives

Xuzhi Zhang
Shaohui Peng
Qirui Zhou
Yuanbo Wen
Qi Guo
Ruizhi Chen
Xinguo Zhu
Weiqiang Xiong

Computation-intensive tensor operators constitute over 90% of the computations in Large Language Models (LLMs) and Deep Neural Networks. Automatically and efficiently generating high-performance tensor operators with hardware primitives is crucial for diverse and ever-evolving hardware architectures like RISC-V, ARM, and GPUs, as manually optimized implementation takes at least months and lacks portability. LLMs excel at generating high-level language codes, but they struggle to fully comprehend hardware characteristics and produce high-performance tensor operators. We introduce a tensor-operator auto-generation framework with a one-line user prompt (QiMeng-TensorOp), which enables LLMs to automatically exploit hardware characteristics to generate tensor operators with hardware primitives, and tune parameters for optimal performance across diverse hardware. Experimental results on various hardware platforms, SOTA LLMs, and typical tensor operators demonstrate that QiMeng-TensorOp effectively unleashes the computing capability of various hardware platforms, and automatically generates tensor operators of superior performance. Compared with vanilla LLMs, QiMeng-TensorOp achieves up to 1291× performance improvement. Even compared with human experts, QiMeng-TensorOp could reach 251% of OpenBLAS on RISC-V CPUs, and 124% of cuBLAS on NVIDIA GPUs. Additionally, QiMeng-TensorOp also significantly reduces development costs by 200× compared with human experts.

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NeurIPS Conference 2025 Conference Paper

SciArena: An Open Evaluation Platform for Non-Verifiable Scientific Literature-Grounded Tasks

Yilun Zhao
Kaiyan Zhang
Tiansheng Hu
Sihong Wu
Ronan Le Bras
Yixin Liu
Robert Tang
Joseph Chee Chang

We present SciArena, an open and collaborative platform for evaluating foundation models on scientific literature-grounded tasks. Unlike traditional benchmarks for scientific literature understanding and synthesis, SciArena engages the research community directly, following the Chatbot Arena evaluation approach of community voting on model comparisons. By leveraging collective intelligence, SciArena offers a community-driven evaluation of model performance on open-ended scientific tasks that demand literature-grounded, long-form responses. The platform currently supports 44 open-source and proprietary foundation models and has collected over 19, 000 votes from human researchers across diverse scientific domains. Our analysis of the data collected so far confirms its high quality. We discuss the results and insights based on the model ranking leaderboard. To further promote research in building model-based automated evaluation systems for literature tasks, we release SciArena-Eval, a meta-evaluation benchmark based on our collected preference data. The benchmark measures the accuracy of models in judging answer quality by comparing their pairwise assessments with human votes. Our experiments highlight the benchmark’s challenges and emphasize the need for more reliable automated evaluation methods.

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NeurIPS Conference 2025 Conference Paper

SolverLLM: Leveraging Test-Time Scaling for Optimization Problem via LLM-Guided Search

Dong Li
Xujiang Zhao
Linlin Yu
Yanchi Liu
Wei Cheng
Zhengzhang Chen
Zhong Chen
Feng Chen

Large Language Models (LLMs) offer promising capabilities for tackling complex reasoning tasks, including optimization problems. However, existing methods either rely on prompt engineering, which leads to poor generalization across problem types, or require costly supervised training. We introduce SolverLLM, a training-free framework that leverages test-time scaling to solve diverse optimization problems. Rather than solving directly, SolverLLM generates mathematical formulations and translates them into solver-ready code, guided by a novel Monte Carlo Tree Search (MCTS) strategy. To enhance the search process, we modify classical MCTS with (1) dynamic expansion for adaptive formulation generation, (2) prompt backpropagation to guide exploration via outcome-driven feedback, and (3) uncertainty backpropagation to incorporate reward reliability into decision-making. Experiments on six standard benchmark datasets demonstrate that SolverLLM outperforms both prompt-based and learning-based baselines, achieving strong generalization without additional training.

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NeurIPS Conference 2025 Conference Paper

UltraHR-100K: Enhancing UHR Image Synthesis with A Large-Scale High-Quality Dataset

Chen Zhao
En Ci
Yunzhe Xu
Tiehan Fan
Shanyan Guan
Yanhao Ge
Jian Yang
Ying Tai

Ultra-high-resolution (UHR) text-to-image (T2I) generation has seen notable progress. However, two key challenges remain: 1) the absence of a large-scale high-quality UHR T2I dataset, and (2) the neglect of tailored training strategies for fine-grained detail synthesis in UHR scenarios. To tackle the first challenge, we introduce \textbf{UltraHR-100K}, a high-quality dataset of 100K UHR images with rich captions, offering diverse content and strong visual fidelity. Each image exceeds 3K resolution and is rigorously curated based on detail richness, content complexity, and aesthetic quality. To tackle the second challenge, we propose a frequency-aware post-training method that enhances fine-detail generation in T2I diffusion models. Specifically, we design (i) \textit{Detail-Oriented Timestep Sampling (DOTS)} to focus learning on detail-critical denoising steps, and (ii) \textit{Soft-Weighting Frequency Regularization (SWFR)}, which leverages Discrete Fourier Transform (DFT) to softly constrain frequency components, encouraging high-frequency detail preservation. Extensive experiments on our proposed UltraHR-eval4K benchmarks demonstrate that our approach significantly improves the fine-grained detail quality and overall fidelity of UHR image generation. The code is available at \href{https: //github. com/NJU-PCALab/UltraHR-100k}{here}.

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JAIR Journal 2024 Journal Article

Efficiently Adapt to New Dynamic via Meta-Model

Kaixin Huang
Chen Zhao
Chun Yuan

We delve into the realm of offline meta-reinforcement learning (OMRL), a practical paradigm in the field of reinforcement learning that leverages offline data to adapt to new tasks. While prior approaches have not explored the utilization of context-based dynamical models to tackle OMRL problems, our research endeavors to fill this gap. Our investigation uncovers shortcomings in existing context-based methods, primarily related to distribution shifts during offline learning and challenges in establishing stable task representations. To address these issues, we formulate the problem as Hidden-Parameter MDPs and propose a framework for effective model adaptation using meta-models plus latent variables, which is inferred by the transformer-based system recognition module trained in an unsupervised fashion. Through extensive experimentation encompassing diverse simulated robotics and control tasks, we validate the efficacy of our approach and demonstrate its superior generalization ability compared to existing schemes, and explore multiple strategies for obtaining policies with personalized models. Our method achieves a model with reduced prediction error, outperforming previous methods in policy performance, and facilitating efficient adaptation when compared to prior dynamic model generalization methods and OMRL algorithms.

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NeurIPS Conference 2024 Conference Paper

OpenGaussian: Towards Point-Level 3D Gaussian-based Open Vocabulary Understanding

Yanmin Wu
Jiarui Meng
Haijie Li
Chenming Wu
Yahao Shi
Xinhua Cheng
Chen Zhao
Haocheng Feng

This paper introduces OpenGaussian, a method based on 3D Gaussian Splatting (3DGS) that possesses the capability for 3D point-level open vocabulary understanding. Our primary motivation stems from observing that existing 3DGS-based open vocabulary methods mainly focus on 2D pixel-level parsing. These methods struggle with 3D point-level tasks due to weak feature expressiveness and inaccurate 2D-3D feature associations. To ensure robust feature presentation and 3D point-level understanding, we first employ SAM masks without cross-frame associations to train instance features with 3D consistency. These features exhibit both intra-object consistency and inter-object distinction. Then, we propose a two-stage codebook to discretize these features from coarse to fine levels. At the coarse level, we consider the positional information of 3D points to achieve location-based clustering, which is then refined at the fine level. Finally, we introduce an instance-level 3D-2D feature association method that links 3D points to 2D masks, which are further associated with 2D CLIP features. Extensive experiments, including open vocabulary-based 3D object selection, 3D point cloud understanding, click-based 3D object selection, and ablation studies, demonstrate the effectiveness of our proposed method. The source code is available at our project page https: //3d-aigc. github. io/OpenGaussian.

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JBHI Journal 2024 Journal Article

Sleep Stage Classification Via Multi-View Based Self-Supervised Contrastive Learning of EEG

Chen Zhao
Wei Wu
Haoyi Zhang
Ruiyan Zhang
Xinyue Zheng
Xiangzeng Kong

Self-supervised learning (SSL) is a challenging task in sleep stage classification (SSC) that is capable of mining valuable representations from unlabeled data. However, traditional SSL methods typically focus on single-view learning and do not fully exploit the interactions among information across multiple views. In this study, we focused on a multi-domain view of the same EEG signal and developed a self-supervised multi-view representation learning framework via time series and time–frequency contrasting (MV-TTFC). In the MV-TTFC framework, we built-in a cross-domain view contrastive learning prediction task to establish connections between the temporal view and time–frequency (TF) view, thereby enhancing the information exchange between multiple views. In addition, to improve the quality of the TF view inputs, we introduced an enhanced multisynchrosqueezing transform, which can create high energy concentration TF image views to compensate for the inaccurate representations in traditional TF processing techniques. Finally, integrating temporal, TF, and fusion space contrastive learning effectively captured the latent features in EEG signals. We evaluated MV-TTFC based on two real-world SSC datasets (SleepEDF-78 and SHHS) and compared it with baseline methods in downstream tasks. Our method exhibited state-of-the-art performance, achieving accuracies of 78. 64% and 81. 45% with SleepEDF-78 and SHHS, respectively, and macro F1-scores of 70. 39% with SleepEDF-78 and 70. 47% with SHHS.

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IJCAI Conference 2024 Conference Paper

Supervised Algorithmic Fairness in Distribution Shifts: A Survey

Minglai Shao
Dong Li
Chen Zhao
Xintao Wu
Yujie Lin
Qin Tian

Supervised fairness-aware machine learning under distribution shifts is an emerging field that addresses the challenge of maintaining equitable and unbiased predictions when faced with changes in data distributions from source to target domains. In real-world applications, machine learning models are often trained on a specific dataset but deployed in environments where the data distribution may shift over time due to various factors. This shift can lead to unfair predictions, disproportionately affecting certain groups characterized by sensitive attributes, such as race and gender. In this survey, we provide a summary of various types of distribution shifts and comprehensively investigate existing methods based on these shifts, highlighting six commonly used approaches in the literature. Additionally, this survey lists publicly available datasets and evaluation metrics for empirical studies. We further explore the interconnection with related research fields, discuss the significant challenges, and identify potential directions for future studies.

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IJCAI Conference 2024 Conference Paper

Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments

Yujie Lin
Chen Zhao
Minglai Shao
Baoluo Meng
Xujiang Zhao
Haifeng Chen

Recognizing domain generalization as a commonplace challenge in machine learning, data distribution might progressively evolve across a continuum of sequential domains in practical scenarios. While current methodologies primarily concentrate on bolstering model effectiveness within these new domains, they tend to neglect issues of fairness throughout the learning process. In response, we propose an innovative framework known as Disentanglement for Counterfactual Fairness-aware Domain Generalization (DCFDG). This approach adeptly removes domain-specific information and sensitive information from the embedded representation of classification features. To scrutinize the intricate interplay between semantic information, domain-specific information, and sensitive attributes, we systematically partition the exogenous factors into four latent variables. By incorporating fairness regularization, we utilize semantic information exclusively for classification purposes. Empirical validation on synthetic and authentic datasets substantiates the efficacy of our approach, demonstrating elevated accuracy levels while ensuring the preservation of fairness amidst the evolving landscape of continuous domains.

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TMLR Journal 2024 Journal Article

Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs

Angelica Chen
Jason Phang
Alicia Parrish
Vishakh Padmakumar
Chen Zhao
Samuel R. Bowman
Kyunghyun Cho

Large language models (LLMs) have achieved widespread success on a variety of in-context few-shot tasks, but this success is typically evaluated via correctness rather than consistency. We argue that self-consistency is an important criteria for valid multi-step reasoning in tasks where the solution is composed of the answers to multiple sub-steps. We propose two types of self-consistency that are particularly important for multi-step reasoning -- hypothetical consistency (a model's ability to predict what its output would be in a hypothetical other context) and compositional consistency (consistency of a model's final outputs when intermediate sub-steps are replaced with the model's outputs for those steps). We demonstrate that multiple variants of the GPT-3/-4 models exhibit poor consistency rates across both types of consistency on a variety of tasks.

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AAAI Conference 2023 Conference Paper

Fair Representation Learning for Recommendation: A Mutual Information Perspective

Chen Zhao
Le Wu
Pengyang Shao
Kun Zhang
Richang Hong
Meng Wang

Recommender systems have been widely used in recent years. By exploiting historical user-item interactions, recommender systems can model personalized potential interests of users and have been widely applied to a wide range of scenarios. Despite their impressive performance, most of them may be subject to unwanted biases related to sensitive attributes (e.g., race and gender), leading to unfairness. An intuitive idea to alleviate this problem is to ensure that there is no mutual information between recommendation results and sensitive attributes. However, keeping independence conditions solely achieves fairness improvement while causing an obvious degradation of recommendation accuracy, which is not a desired result. To this end, in this paper, we re-define recommendation fairness with a novel two-fold mutual information objective. In concerned details, we define fairness as mutual information minimization between embeddings and sensitive information, and mutual information maximization between embeddings and non-sensitive information. Then, a flexible Fair Mutual Information (FairMI) framework is designed to achieve this goal. FairMI first employs a sensitive attribute encoder to capture sensitive information in the data. Then, based on results from the sensitive attribute encoder, an interest encoder is developed to generate sensitive-free embeddings, which are expected to contain rich non-sensitive information of input data. Moreover, we propose novel mutual information (upper/lower) bounds with contrastive information estimation for model optimization. Extensive experiments over two real-world datasets demonstrate the effectiveness of our proposed FairMI in reducing unfairness and improving recommendation accuracy simultaneously.

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AAAI Conference 2022 Conference Paper

A Nested Bi-level Optimization Framework for Robust Few Shot Learning

Krishnateja Killamsetty
Changbin Li
Chen Zhao
Feng Chen
Rishabh Iyer

Model-Agnostic Meta-Learning (MAML), a popular gradientbased meta-learning framework, assumes that the contribution of each task or instance to the meta-learner is equal. Hence, it fails to address the domain shift between base and novel classes in few-shot learning. In this work, we propose a novel robust meta-learning algorithm, NESTEDMAML, which learns to assign weights to training tasks or instances. We consider weights as hyper-parameters and iteratively optimize them using a small set of validation tasks set in a nested bi-level optimization approach (in contrast to the standard bi-level optimization in MAML). We then apply NESTED- MAML in the meta-training stage, which involves (1) several tasks sampled from a distribution different from the meta-test task distribution, or (2) some data samples with noisy labels. Extensive experiments on synthetic and real-world datasets demonstrate that NESTEDMAML efficiently mitigates the effects of ”unwanted” tasks or instances, leading to significant improvement over the state-of-the-art robust meta-learning methods.

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IS Journal 2022 Journal Article

Foundation Models for Transportation Intelligence: ITS Convergence in TransVerse

Chen Zhao
Xingyuan Dai
Yisheng Lv
Yonglin Tian
Yuhai Ren
Fei-Yue Wang

Smart cities are our aspiration for a better life where transportation intelligence is indispensable. Recent technological advances in intelligent transportation systems have opened up new possibilities for smart mobility in smart cities. Here we present TengYun, a transportation foundation model designed and developed with parallel learning and federated intelligence for our transportation metaverse called TransVerse. TengYun enables decentralized/distributed autonomous organizations with decentralized/ distributed operations, as well as various federated technologies, from federated security, federated control, federated management, federated services, to federated ecology for transportation intelligence in smart cities. An example for a federation of transportation transformers is discussed for illustrating the operating procedure of TengYun.

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JBHI Journal 2018 Journal Article

An Integrated Maximum Current Density Approach for Noninvasive Detection of Myocardial Infarction

Chen Zhao
Shiqin Jiang
Yanhua Wu
Junjie Zhu
Dafang Zhou
Birgit Hailer
Dietrich Gronemeyer
Peter Van Leeuwen

We present a new approach of integrated maximum current density (IMCD) for the noninvasive detection of myocardial infarction (MI) using magnetocardiography (MCG) data acquired from a superconducting quantum interference device (SQUID) system. In this paper, we investigated the relationship of the maximum current density (MCD) in the current density map and the underlying equivalent current dipole (ECD) based on a novel method of reconstructing the ECD in the extremum circle of the magnetic field map. The performance of IMCD and the integrated ECD (IECD) approaches were also evaluated by using 61-channel MCG data from 39 healthy subjects and 102 patients with ST elevation myocardial infarction (STEMI). Statistical analysis of the healthy and STEMI groups demonstrate that the IMCD approach obtains sensitivity and specificity up to 91. 2% and 84. 6%, somewhat higher than that of IECD, respectively. The results indicate that IMCD provides spatiotemporal information regarding cardiac electrical activity during ventricular repolarization. This approach may be helpful to diagnose MI in clinic application. The physical concept of the approach is also explained in this paper.

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YNIMG Journal 2013 Journal Article

Edited magnetic resonance spectroscopy detects an age-related decline in brain GABA levels

Fei Gao
Richard A.E. Edden
Muwei Li
Nicolaas A.J. Puts
Guangbin Wang
Cheng Liu
Bin Zhao
Huiquan Wang

Gamma-aminobutyric acid (GABA) is the primary inhibitory neurotransmitter in the brain. Although measurements of GABA levels in vivo in the human brain using edited proton magnetic resonance spectroscopy (1H-MRS) have been established for some time, it is has not been established how regional GABA levels vary with age in the normal human brain. In this study, 49 healthy men and 51 healthy women aged between 20 and 76years were recruited and J-difference edited spectra were recorded at 3T to determine the effect of age on GABA levels, and to investigate whether there are regional and gender differences in GABA in mesial frontal and parietal regions. Because the signal detected at 3. 02ppm using these experimental parameters is also expected to contain contributions from both macromolecules (MM) and homocarnosine, in this study the signal is labeled GABA+ rather than GABA. Significant negative correlations were observed between age and GABA+ in both regions studied (GABA+/Cr: frontal region, r=−0. 68, p<0. 001, parietal region, r=−0. 54, p<0. 001; GABA+/NAA: frontal region, r=−0. 58, p<0. 001, parietal region, r=−0. 49, p<0. 001). The decrease in GABA+ with age in the frontal region was more rapid in women than men. Evidence of a measureable decline in GABA is important in considering the neurochemical basis of the cognitive decline that is associated with normal aging.

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