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Yang Shen

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12 papers

2 author rows

AAAI Conference 2026 Conference Paper

HyCoRA: Hyper-Contrastive Role-Adaptive Learning for Role-Playing

Shihao Yang
Zhicong Lu
Yong Yang
Bo Lv
Yang Shen
Nayu Liu

Multi-character role-playing aims to equip models with the capability to simulate diverse roles. Existing methods either use one shared parameterized module across all roles or assign a separate parameterized module to each role. However, the role-shared module may ignore distinct traits of each role, weakening personality learning, while the role-specific module may overlook shared traits across multiple roles, hindering commonality modeling. In this paper, we propose a novel HyCoRA: Hyper-Contrastive Role-Adaptive learning framework, which efficiently improves multi-character role-playing agents' ability by balancing the learning of distinct and shared traits. Specifically, we propose a Hyper-Half Low-Rank Adaptation structure, where one half is a role-specific module generated by a lightweight hyper-network, and the other half is a trainable role-shared module. The role-specific module is devised to represent distinct persona signatures, while the role-shared module serves to capture common traits. Moreover, to better reflect distinct personalities across different roles, we design a hyper-contrastive learning mechanism to help the hyper-network distinguish their unique characteristics. Extensive experimental results on both English and Chinese available benchmarks demonstrate the superiority of our framework. Further GPT-4 evaluations and visual analyses also verify the capability of HyCoRA to capture role characteristics.

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EAAI Journal 2026 Journal Article

Machine-learning-based pattern recognition and key discharge mode diagnosis of multi-source discharge in a real switch cabinet: A perspective from optical signals

Hongtu Cheng
Yang Shen
Qi Hu
Jie Feng
Lei Cai
Xi Zhu
Zhi Fang

Partial discharge (PD) in gas-insulated switch cabinets exhibits spatial distribution, multiple sources, and diverse types, posing challenges for fault diagnosis. The existing detection and pattern recognition methods are unable to effectively address the issues of distributed detection and signal interference that arise when PD co-occurs at multiple locations within the equipment. Fluorescence optical fiber was employed to acquire optical signals corresponding to four typical types of PDs and their 15 spatially distributed multi-source combinations in a real switch cabinet. Discharge data for 15 types, with 1000 cycles per type, totaling 15000 cycles, were collected. The dataset was split into training and testing sets at an 8: 2 ratio, ensuring no overlap between them. To accurately classify the complex discharge modes, we developed a machine learning-based pattern recognition model. This model integrates extreme gradient boosting (XGB) and light gradient boosting machine (LGBM) via a soft voting ensemble, following Bayesian hyperparameter optimization. A dynamic weight adjustment mechanism was also incorporated to address class imbalance. Results demonstrate that the integrated model achieved an overall classification accuracy of 93%. The number of fault types with a diagnostic rate below 90% decreased from 4 to 2, with the diagnostic rate for all fault types exceeding 80%. Recall and F1 scores for most categories remain above 90%, indicating strong performance in identifying multiple types of PD under realistic operational conditions. This research provides a reference for real-time monitoring and intelligent early warning of multi-source PDs in power equipment.

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AIIM Journal 2026 Journal Article

Multi-annotation agreement and prediction consistency networks: Improving semi-supervised segmentation of medical images with ambiguous boundaries

Shuai Wang
Tengjin Weng
Jingyi Wang
Kai Zhao
Yang Shen
Zhidong Zhao
Yixiu Liu
Pengfei Jiao

Medical image segmentation annotations exhibit variations among experts due to the ambiguous boundaries of segmented objects and backgrounds in medical images. Although using multiple annotations for each image in the fully-supervised setting has been extensively studied for training deep models, obtaining a large amount of multi-annotated data is challenging due to the substantial time and manpower costs required for segmentation annotations, resulting in most images lacking any annotations. To address this, we propose Multi-annotated Semi-supervised Ensemble Networks (MSE-Nets) for learning segmentation from limited multi-annotated and abundant unannotated data. Specifically, we introduce the Network Pairwise Consistency Enhancement (NPCE) module and Multi-Network Pseudo Supervised (MNPS) module to enhance MSE-Nets for the segmentation task by considering two major factors: (1) to optimize the utilization of all accessible multi-annotated data, the NPCE separates (dis)agreement annotations of multi-annotated data at the pixel level and handles agreement and disagreement annotations in different ways; (2) to mitigate the introduction of imprecise pseudo-labels, the MNPS extends the training data by leveraging consistent pseudo-labels from unannotated data. Finally, we improve confidence calibration by averaging the predictions of base networks. Experiments on the ISIC dataset show that we reduced the demand for multi-annotated data by 97. 75% and narrowed the gap with the best fully-supervised baseline to just a Jaccard index of 3. 7%. Furthermore, compared to other semi-supervised methods that rely only on a single annotation or a combined fusion approach, the comprehensive experimental results on ISIC and RIGA datasets demonstrate the superior performance of our proposed method in medical image segmentation with ambiguous boundaries.

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NeurIPS Conference 2025 Conference Paper

V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception

Lei Yang
Xinyu Zhang
Jun Li
Chen Wang
Jiaqi Ma
Zhiying Song
Tong Zhao
Ziying Song

Modern autonomous vehicle perception systems often struggle with occlusions and limited perception range. Previous studies have demonstrated the effectiveness of cooperative perception in extending the perception range and overcoming occlusions, thereby enhancing the safety of autonomous driving. In recent years, a series of cooperative perception datasets have emerged; however, these datasets primarily focus on cameras and LiDAR, neglecting 4D Radar—a sensor used in single-vehicle autonomous driving to provide robust perception in adverse weather conditions. In this paper, to bridge the gap created by the absence of 4D Radar datasets in cooperative perception, we present V2X-Radar, the first large-scale, real-world multi-modal dataset featuring 4D Radar. V2X-Radar dataset is collected using a connected vehicle platform and an intelligent roadside unit equipped with 4D Radar, LiDAR, and multi-view cameras. The collected data encompasses sunny and rainy weather conditions, spanning daytime, dusk, and nighttime, as well as various typical challenging scenarios. The dataset consists of 20K LiDAR frames, 40K camera images, and 20K 4D Radar data, including 350K annotated boxes across five categories. To support various research domains, we have established V2X-Radar-C for cooperative perception, V2X-Radar-I for roadside perception, and V2X-Radar-V for single-vehicle perception. Furthermore, we provide comprehensive benchmarks across these three sub-datasets.

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IROS Conference 2024 Conference Paper

Development of a Bilateral Control Teleoperation System for Bipedal Humanoid Robot Utilizing Foot Sole Haptics Feedback

Yang Shen
Masanobu Kanazawa
Kazuki Mori
Ryu Isono
Yuri Nakazawa
Atsuo Takanishi
Takuya Otani

Teleoperating bipedal humanoid robots presents unique challenges, including decreased stability and reduced operator presence. This paper addresses these challenges by proposing a method that leverages the operator’s inherent sense of stability by feedback from a sole haptics display to operate a bipedal humanoid robot. We developed a bilateral control system that integrates a device replicating sole haptics feedback and provides the operator with feedback on changes in the robot’s center of gravity. We conducted operating experiments in the forward-backward direction to evaluate its effectiveness and investigate the effectiveness of sole haptics on robot operation. The results demonstrate that operating with both vision and sole haptics feedback significantly reduces the robot’s fall rate by over 56% when disturbances are applied, compared to using only vision feedback. Moreover, operators reported a 21% higher sense of presence with both vision and sole haptics feedback compared to using only vision feedback.

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NeurIPS Conference 2024 Conference Paper

Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation

Jiwoong Park
Yang Shen

How can diffusion models process 3D geometries in a coarse-to-fine manner, akin to our multiscale view of the world? In this paper, we address the question by focusing on a fundamental biochemical problem of generating 3D molecular conformers conditioned on molecular graphs in a multiscale manner. Our approach consists of two hierarchical stages: i) generation of coarse-grained fragment-level 3D structure from the molecular graph, and ii) generation of fine atomic details from the coarse-grained approximated structure while allowing the latter to be adjusted simultaneously. For the challenging second stage, which demands preserving coarse-grained information while ensuring SE(3) equivariance, we introduce a novel generative model termed Equivariant Blurring Diffusion (EBD), which defines a forward process that moves towards the fragment-level coarse-grained structure by blurring the fine atomic details of conformers, and a reverse process that performs the opposite operation using equivariant networks. We demonstrate the effectiveness of EBD by geometric and chemical comparison to state-of-the-art denoising diffusion models on a benchmark of drug-like molecules. Ablation studies draw insights on the design of EBD by thoroughly analyzing its architecture, which includes the design of the loss function and the data corruption process. Codes are released at https: //github. com/Shen-Lab/EBD.

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EAAI Journal 2023 Journal Article

Intelligent mining of safety hazard information from construction documents using semantic similarity and information entropy

Dan Tian
Mingchao Li
Yang Shen
Shuai Han

Project construction on-site is known to be very dangerous workplace environments due to large numbers of safety hazards. Analysis of construction safety hazards is essential to formulate rational safety management plans and prevent accidents. Construction documents contain large volumes of safety hazard information available for analysis. However, such analyses are challenging because the safety hazard information in the construction documents is presented in an unstructured or semi-structured format. This study proposes a method for intelligent mining of safety hazard information, which comprises safety hazard technical term recognition and safety hazard information analysis. The safety hazard technical term recognition model is developed based on semantic similarity and information correlation to build a safety hazard technical term library. The safety hazard information based on the technical term library is mined and analyzed using the term frequency-inverse document frequency method (TF-IDF). Finally, the proposed method is applied to build the safety hazard technical term library, which contains 2697 technical terms, and develop a hydraulic project construction safety hazard analysis system, which can realize the intelligent recognition and application of technical terms. Meanwhile, this system can automatically extract safety hazard information and provide a visualization interface to intuitively show the safety hazard analysis results, which improves the extraction efficiency of safety hazard information. The study provides a new approach for recognizing technical terms and mining safety hazard information, which can lead to enhancing management efficiency and practical knowledge discovery for safety management.

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NeurIPS Conference 2022 Conference Paper

Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative

Tianxin Wei
Yuning You
Tianlong Chen
Yang Shen
Jingrui He
Zhangyang Wang

This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (we refer to it as HyperGCL). We focus on the following question: How to construct contrastive views for hypergraphs via augmentations? We provide the solutions in two folds. First, guided by domain knowledge, we fabricate two schemes to augment hyperedges with higher-order relations encoded, and adopt three vertex augmentation strategies from graph-structured data. Second, in search of more effective views in a data-driven manner, we for the first time propose a hypergraph generative model to generate augmented views, and then an end-to-end differentiable pipeline to jointly learn hypergraph augmentations and model parameters. Our technical innovations are reflected in designing both fabricated and generative augmentations of hypergraphs. The experimental findings include: (i) Among fabricated augmentations in HyperGCL, augmenting hyperedges provides the most numerical gains, implying that higher-order information in structures is usually more downstream-relevant; (ii) Generative augmentations do better in preserving higher-order information to further benefit generalizability; (iii) HyperGCL also boosts robustness and fairness in hypergraph representation learning. Codes are released at https: //github. com/weitianxin/HyperGCL.

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IJCAI Conference 2022 Conference Paper

Webly-Supervised Fine-Grained Recognition with Partial Label Learning

Yu-Yan Xu
Yang Shen
Xiu-Shen Wei
Jian Yang

The task of webly-supervised fine-grained recognition is to boost recognition accuracy of classifying subordinate categories (e. g. , different bird species) by utilizing freely available but noisy web data. As the label noises significantly hurt the network training, it is desirable to distinguish and eliminate noisy images. In this paper, we propose two strategies, i. e. , open-set noise removal and closed-set noise correction, to both remove such two kinds of web noises w. r. t. fine-grained recognition. Specifically, for open-set noise removal, we utilize a pre-trained deep model to perform deep descriptor transformation to estimate the positive correlation between these web images, and detect the open-set noises based on the correlation values. Regarding closed-set noise correction, we develop a top-k recall optimization loss for firstly assigning a label set towards each web image to reduce the impact of hard label assignment for closed-set noises. Then, we further propose to correct the sample with its label set as the true single label from a partial label learning perspective. Experiments on several webly-supervised fine-grained benchmark datasets show that our method obviously outperforms other existing state-of-the-art methods.

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NeurIPS Conference 2021 Conference Paper

A$^2$-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval

Xiu-Shen Wei
Yang Shen
Xuhao Sun
Han-Jia Ye
Jian Yang

Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images depicting the concept of interests (i. e. , the same sub-category labels) highest based on the fine-grained details in the query. It is desirable to alleviate the challenges of both fine-grained nature of small inter-class variations with large intra-class variations and explosive growth of fine-grained data for such a practical task. In this paper, we propose an Attribute-Aware hashing Network (A$^2$-Net) for generating attribute-aware hash codes to not only make the retrieval process efficient, but also establish explicit correspondences between hash codes and visual attributes. Specifically, based on the captured visual representations by attention, we develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors from the appearance-specific visual representations without attribute annotations. A$^2$-Net is also equipped with a feature decorrelation constraint upon these attribute vectors to enhance their representation abilities. Finally, the required hash codes are generated by the attribute vectors driven by preserving original similarities. Qualitative experiments on five benchmark fine-grained datasets show our superiority over competing methods. More importantly, quantitative results demonstrate the obtained hash codes can strongly correspond to certain kinds of crucial properties of fine-grained objects.

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NeurIPS Conference 2020 Conference Paper

Graph Contrastive Learning with Augmentations

Yuning You
Tianlong Chen
Yongduo Sui
Ting Chen
Zhangyang Wang
Yang Shen

Generalizable, transferrable, and robust representation learning on graph-structured data remains a challenge for current graph neural networks (GNNs). Unlike what has been developed for convolutional neural networks (CNNs) for image data, self-supervised learning and pre-training are less explored for GNNs. In this paper, we propose a graph contrastive learning (GraphCL) framework for learning unsupervised representations of graph data. We first design four types of graph augmentations to incorporate various priors. We then systematically study the impact of various combinations of graph augmentations on multiple datasets, in four different settings: semi-supervised, unsupervised, and transfer learning as well as adversarial attacks. The results show that, even without tuning augmentation extents nor using sophisticated GNN architectures, our GraphCL framework can produce graph representations of similar or better generalizability, transferrability, and robustness compared to state-of-the-art methods. We also investigate the impact of parameterized graph augmentation extents and patterns, and observe further performance gains in preliminary experiments. Our codes are available at https: //github. com/Shen-Lab/GraphCL.

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NeurIPS Conference 2019 Conference Paper

Learning to Optimize in Swarms

Yue Cao
Tianlong Chen
Zhangyang Wang
Yang Shen

Learning to optimize has emerged as a powerful framework for various optimization and machine learning tasks. Current such "meta-optimizers" often learn in the space of continuous optimization algorithms that are point-based and uncertainty-unaware. To overcome the limitations, we propose a meta-optimizer that learns in the algorithmic space of both point-based and population-based optimization algorithms. The meta-optimizer targets at a meta-loss function consisting of both cumulative regret and entropy. Specifically, we learn and interpret the update formula through a population of LSTMs embedded with sample- and feature-level attentions. Meanwhile, we estimate the posterior directly over the global optimum and use an uncertainty measure to help guide the learning process. Empirical results over non-convex test functions and the protein-docking application demonstrate that this new meta-optimizer outperforms existing competitors. The codes are publicly available at: https: //github. com/Shen-Lab/LOIS

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