Weakly_Supervised
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LMILAtt: A Deep Learning Model for Depression Detection from Social Media Users Enhanced by Multi-Instance Learning Based on Attention Mechanism
2025-09-30 11:58:32Yukun YangAbstract
Depression is a major global public health challenge and its early identification is crucial. Social media data provides a new perspective for depression detection, but existing methods face limitations such as insufficient accuracy, insufficient utilization of time series features, and high annotation costs. To this end, this study proposes the LMILAtt model, which innovatively integrates Long Short-Term Memory autoencoders and attention mechanisms: firstly, the temporal dynamic features of user tweets (such as depressive tendency evolution patterns) are extracted through unsupervised LSTM autoencoders. Secondly, the attention mechanism is used to dynamically weight key texts (such as early depression signals) and construct a multi-example learning architecture to improve the accuracy of user-level detection. Finally, the performance was verified on the WU3D dataset labeled by professional medicine. Experiments show that the model is significantly better than the baseline model in terms of accuracy, recall and F1 score. In addition, the weakly supervised learning strategy significantly reduces the cost of labeling and provides an efficient solution for large-scale social media depression screening.
Abstract (translated)
抑郁症是全球公共健康面临的主要挑战，其早期识别至关重要。社交媒体数据为抑郁检测提供了新的视角，但现有方法存在诸如准确性不足、时间序列特征利用不充分以及标注成本高等限制。为此，本研究提出了一种名为LMILAtt的模型，该模型创新性地结合了长短期记忆自动编码器（LSTM autoencoders）和注意力机制：首先，通过无监督的LSTM自动编码器提取用户推文的时间动态特征（例如抑郁倾向演变模式）。其次，利用注意力机制对关键文本进行动态加权（如早期抑郁症信号），并构建多例学习架构以提高用户级别检测的准确性。最后，在由专业医学人员标注的WU3D数据集上验证了模型性能。实验表明，该模型在准确率、召回率和F1值方面均显著优于基准模型。此外，弱监督学习策略大幅降低了标注成本，并为大规模社交媒体抑郁筛查提供了高效的解决方案。
URL
https://arxiv.org/abs/2509.26145
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UniPose: Unified Cross-modality Pose Prior Propagation towards RGB-D data for Weakly Supervised 3D Human Pose Estimation
2025-09-27 15:49:30Jinghong Zheng, Changlong Jiang, Jiaqi Li, Haohong Kuang, Hang Xu, Tingbing YanAbstract
In this paper, we present UniPose, a unified cross-modality pose prior propagation method for weakly supervised 3D human pose estimation (HPE) using unannotated single-view RGB-D sequences (RGB, depth, and point cloud data). UniPose transfers 2D HPE annotations from large-scale RGB datasets (e.g., MS COCO) to the 3D domain via self-supervised learning on easily acquired RGB-D sequences, eliminating the need for labor-intensive 3D keypoint annotations. This approach bridges the gap between 2D and 3D domains without suffering from issues related to multi-view camera calibration or synthetic-to-real data shifts. During training, UniPose leverages off-the-shelf 2D pose estimations as weak supervision for point cloud networks, incorporating spatial-temporal constraints like body symmetry and joint motion. The 2D-to-3D back-projection loss and cross-modality interaction further enhance this process. By treating the point cloud network's 3D HPE results as pseudo ground truth, our anchor-to-joint prediction method performs 3D lifting on RGB and depth networks, making it more robust against inaccuracies in 2D HPE results compared to state-of-the-art methods. Experiments on CMU Panoptic and ITOP datasets show that UniPose achieves comparable performance to fully supervised methods. Incorporating large-scale unlabeled data (e.g., NTU RGB+D 60) enhances its performance under challenging conditions, demonstrating its potential for practical applications. Our proposed 3D lifting method also achieves state-of-the-art results.
Abstract (translated)
在这篇论文中，我们介绍了UniPose，这是一种统一的跨模态姿态先验传播方法，用于使用未标注的单视图RGB-D序列（包括RGB、深度和点云数据）进行弱监督下的三维人体姿态估计（HPE）。UniPose通过在易于获取的RGB-D序列上进行自监督学习，将大规模RGB数据集（如MS COCO）中的2D HPE注释转移到3D领域，从而消除了劳动密集型3D关键点标注的需求。这种方法弥合了2D和3D领域的差距，并避免了与多视角相机校准或合成到现实的数据偏移相关的问题。 在训练过程中，UniPose利用现成的2D姿态估计作为弱监督信号，用于点云网络，并结合空间-时间约束（如身体对称性和关节运动）。2D至3D反投影损失和跨模态交互进一步增强了这一过程。通过将点云网络的3D HPE结果视为伪真实值，我们的锚点到关节预测方法在RGB和深度网络上执行3D提升操作，与最先进的方法相比，在处理2D HPE结果中的不准确性时更为鲁棒。 在CMU Panoptic和ITOP数据集上的实验表明，UniPose达到了与完全监督方法相当的性能。结合大规模未标注数据（如NTU RGB+D 60）提高了其在挑战性条件下的表现，展示了其实用应用的巨大潜力。我们提出的3D提升方法也取得了最新的成果。
URL
https://arxiv.org/abs/2509.23376
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Weakly Supervised Food Image Segmentation using Vision Transformers and Segment Anything Model
2025-09-23 14:01:51Ioannis Sarafis, Alexandros Papadopoulos, Anastasios DelopoulosAbstract
In this paper, we propose a weakly supervised semantic segmentation approach for food images which takes advantage of the zero-shot capabilities and promptability of the Segment Anything Model (SAM) along with the attention mechanisms of Vision Transformers (ViTs). Specifically, we use class activation maps (CAMs) from ViTs to generate prompts for SAM, resulting in masks suitable for food image segmentation. The ViT model, a Swin Transformer, is trained exclusively using image-level annotations, eliminating the need for pixel-level annotations during training. Additionally, to enhance the quality of the SAM-generated masks, we examine the use of image preprocessing techniques in combination with single-mask and multi-mask SAM generation strategies. The methodology is evaluated on the FoodSeg103 dataset, generating an average of 2.4 masks per image (excluding background), and achieving an mIoU of 0.54 for the multi-mask scenario. We envision the proposed approach as a tool to accelerate food image annotation tasks or as an integrated component in food and nutrition tracking applications.
Abstract (translated)
在这篇论文中，我们提出了一种针对食品图像的弱监督语义分割方法，该方法利用了Segment Anything Model (SAM) 的零样本能力和可提示性以及Vision Transformers (ViTs) 的注意力机制。具体来说，我们使用来自ViT的类激活图(CAMs)为SAM生成提示，从而产生适合于食品图像分割的掩码。作为ViT模型的Swin Transformer仅通过图像级别的标注进行训练，无需在训练过程中使用像素级别的注释。 为了提高由SAM生成的掩码的质量，我们还研究了结合单个掩码和多个掩码的SAM生成策略与图像预处理技术相结合的方法。该方法在FoodSeg103数据集上进行了评估，在不包括背景的情况下平均每张图片产生2.4个掩码，并且在多掩码场景下达到了0.54的mIoU。 我们设想提出的这种方法可以作为一种工具来加速食品图像标注任务，或者作为食物和营养追踪应用中的集成组件。
URL
https://arxiv.org/abs/2509.19028
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Prompt-DAS: Annotation-Efficient Prompt Learning for Domain Adaptive Semantic Segmentation of Electron Microscopy Images
2025-09-23 13:26:06Jiabao Chen, Shan Xiong, Jialin PengAbstract
Domain adaptive segmentation (DAS) of numerous organelle instances from large-scale electron microscopy (EM) is a promising way to enable annotation-efficient learning. Inspired by SAM, we propose a promptable multitask framework, namely Prompt-DAS, which is flexible enough to utilize any number of point prompts during the adaptation training stage and testing stage. Thus, with varying prompt configurations, Prompt-DAS can perform unsupervised domain adaptation (UDA) and weakly supervised domain adaptation (WDA), as well as interactive segmentation during testing. Unlike the foundation model SAM, which necessitates a prompt for each individual object instance, Prompt-DAS is only trained on a small dataset and can utilize full points on all instances, sparse points on partial instances, or even no points at all, facilitated by the incorporation of an auxiliary center-point detection task. Moreover, a novel prompt-guided contrastive learning is proposed to enhance discriminative feature learning. Comprehensive experiments conducted on challenging benchmarks demonstrate the effectiveness of the proposed approach over existing UDA, WDA, and SAM-based approaches.
Abstract (translated)
领域自适应分割（DAS）从大规模电子显微镜（EM）图像中识别众多细胞器实例是一种有望提高注释效率的学习方式。受到SAM的启发，我们提出了一种可提示的多任务框架——Prompt-DAS，它在适应性训练阶段和测试阶段都能够灵活地利用任意数量的点提示。因此，通过不同的提示配置，Prompt-DAS可以执行无监督领域自适应（UDA）、弱监督领域自适应（WDA）以及交互式分割。与基础模型SAM不同，后者需要为每个单独的对象实例提供一个提示，Prompt-DAS仅在一个小数据集上进行训练，并且能够利用所有实例的全部点、部分实例的稀疏点或根本不使用任何点，这得益于辅助中心点检测任务的加入。此外，还提出了一种新颖的提示引导对比学习方法以增强判别性特征的学习。在具有挑战性的基准上的全面实验表明，所提出的方法优于现有的UDA、WDA和基于SAM的方法。
URL
https://arxiv.org/abs/2509.18973
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Depth Edge Alignment Loss: DEALing with Depth in Weakly Supervised Semantic Segmentation
2025-09-22 12:42:10Patrick Schmidt, Vasileios Belagiannis, Lazaros NalpantidisAbstract
Autonomous robotic systems applied to new domains require an abundance of expensive, pixel-level dense labels to train robust semantic segmentation models under full supervision. This study proposes a model-agnostic Depth Edge Alignment Loss to improve Weakly Supervised Semantic Segmentation models across different datasets. The methodology generates pixel-level semantic labels from image-level supervision, avoiding expensive annotation processes. While weak supervision is widely explored in traditional computer vision, our approach adds supervision with pixel-level depth information, a modality commonly available in robotic systems. We demonstrate how our approach improves segmentation performance across datasets and models, but can also be combined with other losses for even better performance, with improvements up to +5.439, +1.274 and +16.416 points in mean Intersection over Union on the PASCAL VOC / MS COCO validation, and the HOPE static onboarding split, respectively. Our code will be made publicly available.
Abstract (translated)
自主机器人系统在新领域的应用需要大量的昂贵的像素级密集标签来训练完全监督下的稳健语义分割模型。本研究提出了一种与模型无关的深度边缘对齐损失（Depth Edge Alignment Loss），以改进跨不同数据集的弱监督语义分割模型。该方法通过图像级别的监督生成像素级语义标签，从而避免了昂贵的标注过程。尽管传统计算机视觉中广泛探索了弱监督问题，但我们的方法增加了像素级深度信息作为监督，这种模态在机器人系统中通常可以获取到。我们展示了如何改进不同数据集和模型上的分割性能，并且该方法还可以与其他损失结合使用以获得更好的效果，在PASCAL VOC / MS COCO验证集和HOPE静态上行分集分别提高了+5.439，+1.274和+16.416的平均交并比（mean Intersection over Union）点数。我们的代码将公开发布。
URL
https://arxiv.org/abs/2509.17702
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Domain Adaptation for Ulcerative Colitis Severity Estimation Using Patient-Level Diagnoses
2025-09-18 03:19:50Takamasa Yamaguchi, Brian Kenji Iwana, Ryoma Bise, Shota Harada, Takumi Okuo, Kiyohito Tanaka, Kaito ShikuAbstract
The development of methods to estimate the severity of Ulcerative Colitis (UC) is of significant importance. However, these methods often suffer from domain shifts caused by differences in imaging devices and clinical settings across hospitals. Although several domain adaptation methods have been proposed to address domain shift, they still struggle with the lack of supervision in the target domain or the high cost of annotation. To overcome these challenges, we propose a novel Weakly Supervised Domain Adaptation method that leverages patient-level diagnostic results, which are routinely recorded in UC diagnosis, as weak supervision in the target domain. The proposed method aligns class-wise distributions across domains using Shared Aggregation Tokens and a Max-Severity Triplet Loss, which leverages the characteristic that patient-level diagnoses are determined by the most severe region within each patient. Experimental results demonstrate that our method outperforms comparative DA approaches, improving UC severity estimation in a domain-shifted setting.
Abstract (translated)
开发用于估计溃疡性结肠炎（UC）严重程度的方法具有重要意义。然而，这些方法常常因为不同医院在成像设备和临床设置上的差异而遭受领域偏移的问题。尽管已经提出了几种域适应方法来解决领域偏移问题，但它们仍然面临目标领域缺乏监督或标注成本高昂的挑战。为了解决这些问题，我们提出了一种新颖的弱监督域自适应方法，该方法利用了在UC诊断中常规记录的患者级别的诊断结果作为目标领域的弱监督信号。我们的方法通过共享聚合令牌（Shared Aggregation Tokens）和最大严重度三元组损失（Max-Severity Triplet Loss）跨领域对类别分布进行对齐，这一策略利用了这样一个特性：患者的最终诊断是由每个患者体内最严重的区域决定的。实验结果表明，我们的方法在域偏移设置下优于比较性的域适应方法，从而提高了UC严重程度估计的准确性。
URL
https://arxiv.org/abs/2509.14573
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SAMIR, an efficient registration framework via robust feature learning from SAM
2025-09-17 01:56:35Yue He, Min Liu, Qinghao Liu, Jiazheng Wang, Yaonan Wang, Hang Zhang, Xiang ChenAbstract
Image registration is a fundamental task in medical image analysis. Deformations are often closely related to the morphological characteristics of tissues, making accurate feature extraction crucial. Recent weakly supervised methods improve registration by incorporating anatomical priors such as segmentation masks or landmarks, either as inputs or in the loss function. However, such weak labels are often not readily available, limiting their practical use. Motivated by the strong representation learning ability of visual foundation models, this paper introduces SAMIR, an efficient medical image registration framework that utilizes the Segment Anything Model (SAM) to enhance feature extraction. SAM is pretrained on large-scale natural image datasets and can learn robust, general-purpose visual representations. Rather than using raw input images, we design a task-specific adaptation pipeline using SAM's image encoder to extract structure-aware feature embeddings, enabling more accurate modeling of anatomical consistency and deformation patterns. We further design a lightweight 3D head to refine features within the embedding space, adapting to local deformations in medical images. Additionally, we introduce a Hierarchical Feature Consistency Loss to guide coarse-to-fine feature matching and improve anatomical alignment. Extensive experiments demonstrate that SAMIR significantly outperforms state-of-the-art methods on benchmark datasets for both intra-subject cardiac image registration and inter-subject abdomen CT image registration, achieving performance improvements of 2.68% on ACDC and 6.44% on the abdomen dataset. The source code will be publicly available on GitHub following the acceptance of this paper.
Abstract (translated)
医学图像分析中，图像配准是一项基本任务。变形通常与组织的形态特征密切相关，因此准确地提取特征至关重要。最近的一些弱监督方法通过在输入或损失函数中加入解剖先验（如分割掩码或地标）来改进注册效果。然而，这种弱标签往往不易获得，限制了它们的实际应用。鉴于视觉基础模型强大的表示学习能力，本文介绍了一种名为SAMIR的高效医学图像配准框架，该框架利用Segment Anything Model (SAM) 来增强特征提取。SAM预先在大规模自然图像数据集上训练，并能学习到稳健且通用的视觉表征。 与使用原始输入图像不同，我们设计了一个特定任务适应性流水线，通过使用SAM的图像编码器来提取结构感知特征嵌入，从而能够更准确地建模解剖一致性和变形模式。此外，我们还设计了一种轻量级3D头部，在嵌入空间内细化特性，并适应医学图像中的局部变形。同时，引入了分层特征一致性损失以引导从粗到细的特征匹配并提高解剖对齐。 广泛的实验表明，SAMIR在用于心脏图像配准和腹部CT图像配准的基准数据集上显著优于现有最佳方法，分别在ACDC数据集中提高了2.68%，在腹部数据集中提高了6.44%。论文被接受后，源代码将通过GitHub公开发布。
URL
https://arxiv.org/abs/2509.13629
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Weakly and Self-Supervised Class-Agnostic Motion Prediction for Autonomous Driving
2025-09-16 14:22:45Ruibo Li, Hanyu Shi, Zhe Wang, Guosheng LinAbstract
Understanding motion in dynamic environments is critical for autonomous driving, thereby motivating research on class-agnostic motion prediction. In this work, we investigate weakly and self-supervised class-agnostic motion prediction from LiDAR point clouds. Outdoor scenes typically consist of mobile foregrounds and static backgrounds, allowing motion understanding to be associated with scene parsing. Based on this observation, we propose a novel weakly supervised paradigm that replaces motion annotations with fully or partially annotated (1%, 0.1%) foreground/background masks for supervision. To this end, we develop a weakly supervised approach utilizing foreground/background cues to guide the self-supervised learning of motion prediction models. Since foreground motion generally occurs in non-ground regions, non-ground/ground masks can serve as an alternative to foreground/background masks, further reducing annotation effort. Leveraging non-ground/ground cues, we propose two additional approaches: a weakly supervised method requiring fewer (0.01%) foreground/background annotations, and a self-supervised method without annotations. Furthermore, we design a Robust Consistency-aware Chamfer Distance loss that incorporates multi-frame information and robust penalty functions to suppress outliers in self-supervised learning. Experiments show that our weakly and self-supervised models outperform existing self-supervised counterparts, and our weakly supervised models even rival some supervised ones. This demonstrates that our approaches effectively balance annotation effort and performance.
Abstract (translated)
理解动态环境中物体的运动对于自动驾驶至关重要，从而推动了无类别限制（class-agnostic）运动预测的研究。在这项工作中，我们研究了基于激光雷达点云的弱监督和自监督无类别限制运动预测方法。室外场景通常由移动前景和静态背景组成，这使得对运动的理解可以与场景解析相关联。基于这一观察，我们提出了一种新颖的弱监督范式，用完全或部分标注（1%，0.1%）的前景/背景掩码来替代运动标注以进行监督。 为此，我们开发了一种利用前景/背景线索指导自监督学习的方法，用于运动预测模型。鉴于前景运动通常发生在非地面区域中，非地面/地面掩模可以作为前景/背景掩模的替代品使用，进一步减少注释工作量。基于非地面/地面提示，我们提出了两种额外方法：一种弱监督方法需要更少（0.01%）的前景/背景标注，以及一种无需任何标注的自监督方法。 此外，为了增强自监督学习中的鲁棒性和一致性，我们设计了一种新颖的Robust Consistency-aware Chamfer Distance损失函数。该函数融合了多帧信息，并使用鲁棒惩罚函数来抑制异常值。 实验结果表明，我们的弱监督和自监督模型优于现有的自监督方法，甚至在某些情况下，我们的弱监督模型与一些有监督的方法性能相当。这证明了我们提出的方法能够在标注工作量和性能之间找到有效的平衡点。
URL
https://arxiv.org/abs/2509.13116
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Instance-Guided Class Activation Mapping for Weakly Supervised Semantic Segmentation
2025-09-15 22:41:44Ali Torabi, Sanjog Gaihre, MD Mahbubur Rahman, Yaqoob MajeedAbstract
Weakly Supervised Semantic Segmentation (WSSS) addresses the challenge of training segmentation models using only image-level annotations, eliminating the need for expensive pixel-level labeling. While existing methods struggle with precise object boundary localization and often focus only on the most discriminative regions, we propose IG-CAM (Instance-Guided Class Activation Mapping), a novel approach that leverages instance-level cues and influence functions to generate high-quality, boundary-aware localization maps. Our method introduces three key innovations: (1) Instance-Guided Refinement that uses ground truth segmentation masks to guide CAM generation, ensuring complete object coverage rather than just discriminative parts; (2) Influence Function Integration that captures the relationship between training samples and model predictions, leading to more robust feature representations; and (3) Multi-Scale Boundary Enhancement that employs progressive refinement strategies to achieve sharp, precise object boundaries. IG-CAM achieves state-of-the-art performance on the PASCAL VOC 2012 dataset with an mIoU of 82.3% before post-processing, which further improves to 86.6% after applying Conditional Random Field (CRF) refinement, significantly outperforming previous WSSS methods. Our approach demonstrates superior localization accuracy, with complete object coverage and precise boundary delineation, while maintaining computational efficiency. Extensive ablation studies validate the contribution of each component, and qualitative comparisons across 600 diverse images showcase the method's robustness and generalization capability. The results establish IG-CAM as a new benchmark for weakly supervised semantic segmentation, offering a practical solution for scenarios where pixel-level annotations are unavailable or prohibitively expensive.
Abstract (translated)
弱监督语义分割（WSSS）致力于使用仅包含图像级标注的数据来训练分割模型，从而避免了昂贵的像素级标签需求。尽管现有的方法在精准定位物体边界方面存在挑战，并且通常只关注最具判别力的区域，我们提出了一种新的方法——基于实例引导类激活映射（IG-CAM），这种方法利用了实例级线索和影响函数来生成高质量、具有边界的定位图。 我们的方法引入了三个关键创新点： 1. 实例导向精炼：该步骤使用真实分割掩码来指导CAM的生成，确保完整地覆盖物体而不是仅仅关注最具判别力的部分。 2. 影响函数整合：这种方法捕捉训练样本与模型预测之间的关系，从而获得更稳健的功能表示。 3. 多尺度边界增强：通过渐进式细化策略实现清晰、精确的物体边界。 在PASCAL VOC 2012数据集上，IG-CAM达到了前所未有的性能，在不进行后处理的情况下获得了82.3%的mIoU分数，并且在应用条件随机场（CRF）精炼之后进一步提升到了86.6%，显著超越了之前的WSSS方法。我们的方法展示了优越的定位精度，包括完整的物体覆盖和精确的边界划分，并保持了计算效率。 详细的消融研究验证了每个组件的贡献，并通过跨越600幅不同图像的定性比较展示了该方法的强大鲁棒性和泛化能力。这些结果确立了IG-CAM作为弱监督语义分割的新基准，为那些像素级标注不可用或成本过高的场景提供了一个实用的解决方案。
URL
https://arxiv.org/abs/2509.12496
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End-to-End 4D Heart Mesh Recovery Across Full-Stack and Sparse Cardiac MRI
2025-09-15 16:17:45Yihong Chen, Jiancheng Yang, Deniz Sayin Mercadier, Hieu Le, Juerg Schwitter, Pascal FuaAbstract
Reconstructing cardiac motion from cine CMR sequences is critical for diagnosis, prediction, and intervention. Existing methods rely on complete CMR stacks to infer full heart motion, limiting their utility in intra-procedural scenarios where only sparse observations are available. We present TetHeart, the first end-to-end framework that unifies full 4D multi-structure heart mesh recovery from both offline full-stack acquisitions and intra-procedural sparse-slice observations. Our method leverages deep deformable tetrahedra, an explicit-implicit hybrid representation, to capture shape and motion in a coherent space shared across cardiac structures. It is initialized from high-quality pre-procedural or offline-acquired full stacks to build detailed, patient-specific heart meshes, which can then be updated using whatever slices are available, from full stacks down to a single slice. We further incorporate several key innovations: (i) an attentive mechanism for slice-adaptive 2D-3D feature assembly that dynamically integrates information from arbitrary numbers of slices at any position, combined with a distillation strategy from full-slice to sparse-slice settings to ensure accurate reconstruction under extreme sparsity; and (ii) a two-stage weakly supervised motion learning scheme requiring only keyframe (e.g., ED and ES) annotations. Trained and validated on three large public datasets and externally evaluated zero-shot on additional private interventional and public CMR datasets, TetHeart achieves state-of-the-art accuracy and strong generalization in both pre- and intra-procedural settings.
Abstract (translated)
从电影式CMR序列重建心脏运动对于诊断、预测和干预至关重要。现有的方法依赖于完整的CMR数据栈来推断完整的心脏运动，这限制了它们在只有稀疏观测数据可用的术中场景中的实用性。我们提出了TetHeart，这是第一个端到端框架，它统一了从离线全层获取和术中稀疏切片观察中恢复完整4D多结构心脏网格的任务。 我们的方法利用深度可变形四面体（一种显式隐式混合表示）来捕捉跨越心脏各结构的一致空间中的形状和运动。它通过高质量的术前或离线获取的全栈数据初始化，构建详细的、个性化的患者心脏网格，并可以使用任何可用切片进行更新，从全层到单个切片均可。 我们进一步融入了几个关键创新： (i) 一种注意力机制用于适应性的2D-3D特征组装，在任意位置动态整合来自任意数量切片的信息，并结合由全切片环境到稀疏切片场景的蒸馏策略，以确保在极端稀疏条件下准确重建。 (ii) 一个两阶段弱监督运动学习方案仅需关键帧（如早期舒张期和晚期收缩期）注释。 TetHeart在三个大型公共数据集上进行训练和验证，并在额外的私人介入和公开CMR数据集上进行零样本外部评估，它在术前和术中设置下均实现了最先进的准确性和强大的泛化能力。
URL
https://arxiv.org/abs/2509.12090
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Weakly Supervised Vulnerability Localization via Multiple Instance Learning
2025-09-14 15:11:39Wenchao Gu, Yupan Chen, Yanlin Wang, Hongyu Zhang, Cuiyun Gao, Michael R. LyuAbstract
Software vulnerability detection has emerged as a significant concern in the field of software security recently, capturing the attention of numerous researchers and developers. Most previous approaches focus on coarse-grained vulnerability detection, such as at the function or file level. However, the developers would still encounter the challenge of manually inspecting a large volume of code inside the vulnerable function to identify the specific vulnerable statements for modification, indicating the importance of vulnerability localization. Training the model for vulnerability localization usually requires ground-truth labels at the statement-level, and labeling vulnerable statements demands expert knowledge, which incurs high costs. Hence, the demand for an approach that eliminates the need for additional labeling at the statement-level is on the rise. To tackle this problem, we propose a novel approach called WAVES for WeAkly supervised Vulnerability Localization via multiplE inStance learning, which does not need the additional statement-level labels during the training. WAVES has the capability to determine whether a function is vulnerable (i.e., vulnerability detection) and pinpoint the vulnerable statements (i.e., vulnerability localization). Specifically, inspired by the concept of multiple instance learning, WAVES converts the ground-truth label at the function-level into pseudo labels for individual statements, eliminating the need for additional statement-level labeling. These pseudo labels are utilized to train the classifiers for the function-level representation vectors. Extensive experimentation on three popular benchmark datasets demonstrates that, in comparison to previous baselines, our approach achieves comparable performance in vulnerability detection and state-of-the-art performance in statement-level vulnerability localization.
Abstract (translated)
近期，软件漏洞检测已成为软件安全领域的一个重要议题，吸引了众多研究人员和开发者的关注。以往的大多数方法主要集中在粗粒度的漏洞检测上，例如在函数或文件级别进行检测。然而，开发人员仍面临手动检查脆弱功能内部大量代码以识别需修改的具体脆弱语句这一挑战，这表明了漏洞定位的重要性。为了训练用于漏洞定位的模型，通常需要具有语句级别的真实标签，并且标注脆弱语句则需要专家知识，从而产生了高昂的成本。因此，对无需额外进行语句级标注的方法的需求日益增长。 为了解决这个问题，我们提出了一种新颖的方法叫做 WAVES（通过多实例学习进行弱监督漏洞定位），该方法在训练过程中不需要额外的语句级别标签。WAVES具有判断一个函数是否脆弱（即漏洞检测）以及确定具体的脆弱语句（即漏洞定位）的能力。具体来说，受多重实例学习概念的启发，WAVES将函数级别的真实标签转换为单个语句的伪标签，从而消除了对额外语句级别标注的需求。这些伪标签被用来训练用于函数级表示向量的分类器。 在三个流行基准数据集上的广泛实验表明，与之前的基线方法相比，我们的方法在漏洞检测方面取得了可比性能，在语句级别的漏洞定位方面达到了当前最佳水平。
URL
https://arxiv.org/abs/2509.11312
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Test-Time Warmup for Multimodal Large Language Models
2025-09-12 18:58:42Nikita Rajaneesh, Thomas Zollo, Richard ZemelAbstract
Multimodal Large Language Models (MLLMs) hold great promise for advanced reasoning at the intersection of text and images, yet they have not fully realized this potential. MLLMs typically integrate an LLM, a vision encoder, and a connector that maps the vision encoder's embeddings into the LLM's text embedding space. Although each component is pretrained on massive datasets with billions of samples, the entire multimodal model is typically trained on only thousands (or a few million) samples, which can result in weak performance on complex reasoning tasks. To address these shortcomings, instead of relying on extensive labeled datasets for fine-tuning, we propose a Test-Time Warmup method that adapts the MLLM per test instance by leveraging data from weakly supervised auxiliary tasks. With our approach, we observe a relative performance improvement of 4.03% on MMMU, 5.28% on VQA-Rad, and 1.63% on GQA on the Llama-Vision-Instruct model. Our method demonstrates that 'warming up' before inference can enhance MLLMs' robustness across diverse reasoning tasks.
Abstract (translated)
多模态大型语言模型（MLLMs）在文本和图像交叉领域中的高级推理方面展现出巨大的潜力，但尚未完全实现这一潜能。MLLMs 通常集成了一个大型语言模型（LLM）、一个视觉编码器以及一个将视觉编码器的嵌入映射到 LLM 的文本嵌入空间中的连接器。尽管每个组件都在包含数十亿样本的大规模数据集上进行了预训练，整个多模态模型通常仅在数千个或几百万个样本上进行微调，这可能导致在复杂推理任务上的表现较弱。 为了弥补这些不足，我们提出了一种名为“测试时预热”（Test-Time Warmup）的方法。该方法通过利用来自弱监督辅助任务的数据，在每个测试实例中适应 MLLM 而无需依赖于广泛标记的数据集进行微调。采用我们的方法后，在 Llama-Vision-Instruct 模型上，MMMU 数据集的相对性能提升了 4.03%，VQA-Rad 数据集提升了 5.28%，GQA 数据集提升了 1.63%。 该方法表明，“预热”之后进行推理能够增强 MLLMs 在各种复杂推理任务中的鲁棒性。
URL
https://arxiv.org/abs/2509.10641
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Incongruent Positivity: When Miscalibrated Positivity Undermines Online Supportive Conversations
2025-09-12 12:25:02Leen Almajed, Abeer ALdayelAbstract
In emotionally supportive conversations, well-intended positivity can sometimes misfire, leading to responses that feel dismissive, minimizing, or unrealistically optimistic. We examine this phenomenon of incongruent positivity as miscalibrated expressions of positive support in both human and LLM generated responses. To this end, we collected real user-assistant dialogues from Reddit across a range of emotional intensities and generated additional responses using large language models for the same context. We categorize these conversations by intensity into two levels: Mild, which covers relationship tension and general advice, and Severe, which covers grief and anxiety conversations. This level of categorization enables a comparative analysis of how supportive responses vary across lower and higher stakes contexts. Our analysis reveals that LLMs are more prone to unrealistic positivity through dismissive and minimizing tone, particularly in high-stakes contexts. To further study the underlying dimensions of this phenomenon, we finetune LLMs on datasets with strong and weak emotional reactions. Moreover, we developed a weakly supervised multilabel classifier ensemble (DeBERTa and MentalBERT) that shows improved detection of incongruent positivity types across two sorts of concerns (Mild and Severe). Our findings shed light on the need to move beyond merely generating generic positive responses and instead study the congruent support measures to balance positive affect with emotional acknowledgment. This approach offers insights into aligning large language models with affective expectations in the online supportive dialogue, paving the way toward context-aware and trust preserving online conversation systems.
Abstract (translated)
在情感支持对话中，虽然善意的积极态度有时会误入歧途，导致回应显得轻率、忽视问题或过分乐观，但这种不一致的正面情绪会被视为对情感支持的一种不当表达。我们研究了人类和大型语言模型（LLM）生成回复中的此类现象。 为此，我们在Reddit上收集了一系列具有不同情感强度的真实用户-助手对话，并使用大型语言模型为相同情境生成额外回应。我们将这些对话按强度分为两个层次：轻度层包括关系紧张和一般性建议；重度层则涵盖哀伤和焦虑的对话。这种分类方法使我们能够比较分析在低风险和高风险背景下支持性回应的变化。 我们的研究发现，在高风险背景下，大型语言模型更倾向于通过轻率或忽视问题的态度表现出不切实际的乐观情绪。为了进一步探究这一现象的本质维度，我们在具有强烈与轻微情感反应的数据集上对大型语言模型进行了微调。此外，我们开发了一种弱监督多标签分类器集合（DeBERTa和MentalBERT），该系统在轻度和重度两种类型的担忧中都显示出更准确地检测不一致的正面情绪的能力。 我们的研究结果揭示了超越生成一般积极回应的重要性，并强调需要研究如何平衡情感承认与积极情感的支持措施。这种做法为大型语言模型在在线支持性对话中的情感预期对齐提供了见解，也为开发上下文感知和信任维护的在线交流系统铺平道路。
URL
https://arxiv.org/abs/2509.10184
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Dual-Thresholding Heatmaps to Cluster Proposals for Weakly Supervised Object Detection
2025-09-10 05:12:03Yuelin Guo, Haoyu He, Zhiyuan Chen, Zitong Huang, Renhao Lu, Lu Shi, Zejun Wang, Weizhe ZhangAbstract
Weakly supervised object detection (WSOD) has attracted significant attention in recent years, as it does not require box-level annotations. State-of-the-art methods generally adopt a multi-module network, which employs WSDDN as the multiple instance detection network module and multiple instance refinement modules to refine performance. However, these approaches suffer from three key limitations. First, existing methods tend to generate pseudo GT boxes that either focus only on discriminative parts, failing to capture the whole object, or cover the entire object but fail to distinguish between adjacent intra-class instances. Second, the foundational WSDDN architecture lacks a crucial background class representation for each proposal and exhibits a large semantic gap between its branches. Third, prior methods discard ignored proposals during optimization, leading to slow convergence. To address these challenges, we first design a heatmap-guided proposal selector (HGPS) algorithm, which utilizes dual thresholds on heatmaps to pre-select proposals, enabling pseudo GT boxes to both capture the full object extent and distinguish between adjacent intra-class instances. We then present a weakly supervised basic detection network (WSBDN), which augments each proposal with a background class representation and uses heatmaps for pre-supervision to bridge the semantic gap between matrices. At last, we introduce a negative certainty supervision loss on ignored proposals to accelerate convergence. Extensive experiments on the challenging PASCAL VOC 2007 and 2012 datasets demonstrate the effectiveness of our framework. We achieve mAP/mCorLoc scores of 58.5%/81.8% on VOC 2007 and 55.6%/80.5% on VOC 2012, performing favorably against the state-of-the-art WSOD methods. Our code is publicly available at this https URL.
Abstract (translated)
近年来，弱监督目标检测（WSOD）因其不需要框级别标注而吸引了大量关注。目前最先进的方法通常采用多模块网络结构，其中包括使用WSDDN作为多次实例检测网络模块，并且有多个实例细化模块来提升性能表现。然而，这些方法面临三个关键局限性：首先，现有的方法倾向于生成伪GT（Ground Truth）框，要么只聚焦于具有区分性的部分而未能捕捉整个对象，要么覆盖了整个对象但无法在相邻的同类实例之间进行区分；其次，基础的WSDDN架构缺乏每个提议的关键背景类表示，并且其分支之间的语义差距较大；最后，先前的方法在优化过程中舍弃被忽略的提议，导致收敛速度缓慢。 为了解决这些挑战，我们首先设计了一种热图引导提案选择器（HGPS）算法，利用热图上的双阈值来预选提案，使伪GT框能够同时捕捉整个对象范围并区分相邻同类实例。接下来，我们提出了一种弱监督基本检测网络（WSBDN），它在每个提议中添加了背景类表示，并使用热图进行预先监督以缩小矩阵之间的语义差距。最后，我们在被忽略的提案上引入了一个负确定性监督损失，以加速收敛过程。 在具有挑战性的PASCAL VOC 2007和2012数据集上的广泛实验展示了我们框架的有效性。我们的方法在VOC 2007上获得了58.5%/81.8%的mAP/mCorLoc得分，在VOC 2012上则为55.6%/80.5%，相对于现有的最先进的WSOD方法表现出了明显的优越性。我们的代码可公开获取，地址见文中的链接。 此翻译旨在传达原文的技术内容和研究成果，相关技术细节与专业术语保持不变。
URL
https://arxiv.org/abs/2509.08289
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Attribute-based Object Grounding and Robot Grasp Detection with Spatial Reasoning
2025-09-09 20:07:51Houjian Yu, Zheming Zhou, Min Sun, Omid Ghasemalizadeh, Yuyin Sun, Cheng-Hao Kuo, Arnie Sen, Changhyun ChoiAbstract
Enabling robots to grasp objects specified through natural language is essential for effective human-robot interaction, yet it remains a significant challenge. Existing approaches often struggle with open-form language expressions and typically assume unambiguous target objects without duplicates. Moreover, they frequently rely on costly, dense pixel-wise annotations for both object grounding and grasp configuration. We present Attribute-based Object Grounding and Robotic Grasping (OGRG), a novel framework that interprets open-form language expressions and performs spatial reasoning to ground target objects and predict planar grasp poses, even in scenes containing duplicated object instances. We investigate OGRG in two settings: (1) Referring Grasp Synthesis (RGS) under pixel-wise full supervision, and (2) Referring Grasp Affordance (RGA) using weakly supervised learning with only single-pixel grasp annotations. Key contributions include a bi-directional vision-language fusion module and the integration of depth information to enhance geometric reasoning, improving both grounding and grasping performance. Experiment results show that OGRG outperforms strong baselines in tabletop scenes with diverse spatial language instructions. In RGS, it operates at 17.59 FPS on a single NVIDIA RTX 2080 Ti GPU, enabling potential use in closed-loop or multi-object sequential grasping, while delivering superior grounding and grasp prediction accuracy compared to all the baselines considered. Under the weakly supervised RGA setting, OGRG also surpasses baseline grasp-success rates in both simulation and real-robot trials, underscoring the effectiveness of its spatial reasoning design. Project page: this https URL
Abstract (translated)
使机器人能够通过自然语言抓取指定的物体对于有效的机器人与人交互至关重要，但这一任务仍然面临着重大挑战。现有的方法通常难以处理开放形式的语言表达，并且假定目标物体会很明确且没有重复的对象。此外，它们往往依赖于成本高昂、像素级别的密集标注来为对象定位和抓取配置提供信息。我们提出了基于属性的目标物体定位与机器人抓取（OGRG）这一新颖框架，该框架能够解释开放形式的语言表达，并通过空间推理确定目标物及其平面抓取位置，即使在包含重复实例的场景中也能实现这一点。我们在两种设置下研究了 OGRG：(1) 像素级别监督下的指称抓取合成（RGS），以及 (2) 仅使用单一像素抓取标注进行弱监督学习的指称抓取能力（RGA）。关键贡献包括双向视觉语言融合模块和深度信息集成，以增强几何推理，从而提升定位与抓取性能。实验结果表明，在具有多样空间语言指令的桌面上，OGRG 超过了所有强基线模型的表现。在 RGS 中，它可以在单个 NVIDIA RTX 2080 Ti GPU 上实现每秒17.59帧的操作速度，这使其实现闭环或多物体顺序抓取成为可能，并且其定位和抓取预测准确性超越了所有的基准测试模型。而在弱监督的RGA设置下，OGRG 在模拟和真实机器人实验中也超过了基线的成功抓取率，强调了其空间推理设计的有效性。 项目主页：[此URL](https://this-url.com)
URL
https://arxiv.org/abs/2509.08126
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MVAT: Multi-View Aware Teacher for Weakly Supervised 3D Object Detection
2025-09-09 08:40:54Saad Lahlali, Alexandre Fournier Montgieux, Nicolas Granger, Herv\'e Le Borgne, Quoc Cuong PhamAbstract
Annotating 3D data remains a costly bottleneck for 3D object detection, motivating the development of weakly supervised annotation methods that rely on more accessible 2D box annotations. However, relying solely on 2D boxes introduces projection ambiguities since a single 2D box can correspond to multiple valid 3D poses. Furthermore, partial object visibility under a single viewpoint setting makes accurate 3D box estimation difficult. We propose MVAT, a novel framework that leverages temporal multi-view present in sequential data to address these challenges. Our approach aggregates object-centric point clouds across time to build 3D object representations as dense and complete as possible. A Teacher-Student distillation paradigm is employed: The Teacher network learns from single viewpoints but targets are derived from temporally aggregated static objects. Then the Teacher generates high quality pseudo-labels that the Student learns to predict from a single viewpoint for both static and moving objects. The whole framework incorporates a multi-view 2D projection loss to enforce consistency between predicted 3D boxes and all available 2D annotations. Experiments on the nuScenes and Waymo Open datasets demonstrate that MVAT achieves state-of-the-art performance for weakly supervised 3D object detection, significantly narrowing the gap with fully supervised methods without requiring any 3D box annotations. % \footnote{Code available upon acceptance} Our code is available in our public repository (\href{this https URL}{code}).
Abstract (translated)
标注3D数据仍然是3D物体检测中的一个高成本瓶颈，这促使了开发依赖于更易获取的2D框注释的弱监督标注方法的发展。然而，仅依赖2D框会导致投影歧义问题，因为单一的2D框可以对应多个有效的3D姿态。此外，在单视点设置下，部分物体可见性使得准确估计3D边界框变得困难。我们提出了一种新的框架MVAT（多视角时序聚合），利用序列数据中的时间多视角特性来解决这些问题。我们的方法通过跨时间聚集以对象为中心的点云来构建尽可能密集和完整的3D物体表示。 本研究采用了教师-学生蒸馏范式：教师网络从单视点学习，但目标是从时间上聚合后的静态物体中得出。然后，教师生成高质量的伪标签，供学生在单一视角下进行预测，用于静态和移动物体。整个框架整合了一个多视角2D投影损失函数，以确保预测的3D边界框与所有可用的2D注释之间的一致性。 在nuScenes和Waymo Open数据集上的实验表明，MVAT实现了弱监督条件下3D物体检测的最佳性能，并且显著缩小了与完全监督方法之间的差距，同时无需任何3D框标注。我们的代码可在公共存储库中获取（\href{this https URL}{code}）。
URL
https://arxiv.org/abs/2509.07507
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WS$^2$: Weakly Supervised Segmentation using Before-After Supervision in Waste Sorting
2025-09-08 09:48:37Andrea Marelli, Alberto Foresti, Leonardo Pesce, Giacomo Boracchi, Mario GrossoAbstract
In industrial quality control, to visually recognize unwanted items within a moving heterogeneous stream, human operators are often still indispensable. Waste-sorting stands as a significant example, where operators on multiple conveyor belts manually remove unwanted objects to select specific materials. To automate this recognition problem, computer vision systems offer great potential in accurately identifying and segmenting unwanted items in such settings. Unfortunately, considering the multitude and the variety of sorting tasks, fully supervised approaches are not a viable option to address this challange, as they require extensive labeling efforts. Surprisingly, weakly supervised alternatives that leverage the implicit supervision naturally provided by the operator in his removal action are relatively unexplored. In this paper, we define the concept of Before-After Supervision, illustrating how to train a segmentation network by leveraging only the visual differences between images acquired \textit{before} and \textit{after} the operator. To promote research in this direction, we introduce WS$^2$ (Weakly Supervised segmentation for Waste-Sorting), the first multiview dataset consisting of more than 11 000 high-resolution video frames captured on top of a conveyor belt, including "before" and "after" images. We also present a robust end-to-end pipeline, used to benchmark several state-of-the-art weakly supervised segmentation methods on WS$^2$.
Abstract (translated)
在工业质量控制中，为了在流动的异质物料流中视觉识别不需要的物品，人工操作员通常仍然是不可或缺的。废物分拣是这一领域的显著例子，在此过程中，操作员需通过多条传送带手动移除不必要的物体以选出特定材料。为了解决这个问题实现自动化，计算机视觉系统提供了巨大的潜力，能够准确地在这样的环境下识别和分割不需要的物品。然而，考虑到分类任务的数量和多样性，完全监督的方法由于需要大量的标注工作而不现实。令人惊讶的是，那些利用操作员移除动作提供的隐性监督信息的弱监督方法，在这一领域却相对未被充分探索。 本文中我们定义了“前后监督”（Before-After Supervision）的概念，并展示了如何仅通过使用操作员介入前后的视觉差异来训练分割网络。为促进该领域的研究，我们引入了WS$^2$(Weakly Supervised segmentation for Waste-Sorting)，这是首个包含超过1.1万张高分辨率视频帧的多视图数据集，这些图像是在传送带上捕捉到的操作前后影像。此外，我们还提出了一种稳健的端到端管道，并用其对WS$^2$上几种最先进的弱监督分割方法进行了基准测试。
URL
https://arxiv.org/abs/2509.06485
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Imitation Learning Based on Disentangled Representation Learning of Behavioral Characteristics
2025-09-05 01:31:07Ryoga Oishi, Sho Sakaino, Toshiaki TsujiAbstract
In the field of robot learning, coordinating robot actions through language instructions is becoming increasingly feasible. However, adapting actions to human instructions remains challenging, as such instructions are often qualitative and require exploring behaviors that satisfy varying conditions. This paper proposes a motion generation model that adapts robot actions in response to modifier directives human instructions imposing behavioral conditions during task execution. The proposed method learns a mapping from modifier directives to actions by segmenting demonstrations into short sequences, assigning weakly supervised labels corresponding to specific modifier types. We evaluated our method in wiping and pick and place tasks. Results show that it can adjust motions online in response to modifier directives, unlike conventional batch-based methods that cannot adapt during execution.
Abstract (translated)
在机器人学习领域，通过语言指令协调机器人的行动正变得越来越可行。然而，根据人类的指示调整动作仍然是一项挑战，因为这些指示往往是定性的，并且需要探索能够满足各种条件的行为。本文提出了一种运动生成模型，该模型能够在任务执行过程中根据修饰性的人类指令来适应和调整机器人的行为。所提出的方法通过将演示分割成短序列，并分配与特定修饰类型对应的弱监督标签，学习了从修饰性指示到动作的映射。 我们在擦拭和取物放置等任务中评估了我们的方法。实验结果表明，该模型能够在线响应修饰指令进行运动调整，而传统的基于批次的方法则无法在执行过程中进行适应。
URL
https://arxiv.org/abs/2509.04737
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TEn-CATS: Text-Enriched Audio-Visual Video Parsing with Multi-Scale Category-Aware Temporal Graph
2025-09-04 10:32:40Yaru Chen, Faegheh Sardari, Peiliang Zhang, Ruohao Guo, Yang Xiang, Zhenbo Li, Wenwu WangAbstract
Audio-Visual Video Parsing (AVVP) task aims to identify event categories and their occurrence times in a given video with weakly supervised labels. Existing methods typically fall into two categories: (i) designing enhanced architectures based on attention mechanism for better temporal modeling, and (ii) generating richer pseudo-labels to compensate for the absence of frame-level annotations. However, the first type methods treat noisy segment-level pseudo labels as reliable supervision and the second type methods let indiscriminate attention spread them across all frames, the initial errors are repeatedly amplified during training. To address this issue, we propose a method that combines the Bi-Directional Text Fusion (BiT) module and Category-Aware Temporal Graph (CATS) module. Specifically, we integrate the strengths and complementarity of the two previous research directions. We first perform semantic injection and dynamic calibration on audio and visual modality features through the BiT module, to locate and purify cleaner and richer semantic cues. Then, we leverage the CATS module for semantic propagation and connection to enable precise semantic information dissemination across time. Experimental results demonstrate that our proposed method achieves state-of-the-art (SOTA) performance in multiple key indicators on two benchmark datasets, LLP and UnAV-100.
Abstract (translated)
音频视觉视频解析（Audio-Visual Video Parsing，AVVP）任务旨在通过弱监督标签识别给定视频中的事件类别及其发生时间。现有方法通常分为两类：一类是设计基于注意力机制的增强架构以实现更好的时间建模；另一类是生成更丰富的伪标签来弥补帧级别注释缺失的问题。然而，第一类方法将嘈杂的片段级伪标签视为可靠监督信号，并且第二类方法让无差别注意扩散到所有帧中，在训练过程中初始错误会反复被放大。 为了解决这个问题，我们提出了一种结合双向文本融合（Bi-Directional Text Fusion，BiT）模块和类别感知时间图（Category-Aware Temporal Graph，CATS）模块的方法。具体来说，我们将之前两个研究方向的优势与互补性结合起来。首先，通过BiT模块对音频和视觉模态特征进行语义注入和动态校准，以定位并净化更清洁、更丰富的语义线索。然后，利用CATS模块实现语义传播和连接功能，使精确的语义信息能够在时间维度上传播。 实验结果表明，在LLP和UnAV-100两个基准数据集上的多个关键指标上，我们提出的方法取得了最先进的（State-of-the-Art，SOTA）性能。
URL
https://arxiv.org/abs/2509.04086
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Weakly Supervised Medical Entity Extraction and Linking for Chief Complaints
2025-09-02 02:39:42Zhimeng Luo, Zhendong Wang, Rui Meng, Diyang Xue, Adam Frisch, Daqing HeAbstract
A Chief complaint (CC) is the reason for the medical visit as stated in the patient's own words. It helps medical professionals to quickly understand a patient's situation, and also serves as a short summary for medical text mining. However, chief complaint records often take a variety of entering methods, resulting in a wide variation of medical notations, which makes it difficult to standardize across different medical institutions for record keeping or text mining. In this study, we propose a weakly supervised method to automatically extract and link entities in chief complaints in the absence of human annotation. We first adopt a split-and-match algorithm to produce weak annotations, including entity mention spans and class labels, on 1.2 million real-world de-identified and IRB approved chief complaint records. Then we train a BERT-based model with generated weak labels to locate entity mentions in chief complaint text and link them to a pre-defined ontology. We conducted extensive experiments, and the results showed that our Weakly Supervised Entity Extraction and Linking (\ours) method produced superior performance over previous methods without any human annotation.
Abstract (translated)
主诉（CC）是指患者用自己的语言表达的就诊原因。它有助于医疗专业人员快速了解患者的状况，并且也作为医学文本挖掘的简短摘要使用。然而，由于主诉记录通常采用多种输入方法，导致医疗注释形式多样，这使得在不同的医疗机构之间难以标准化记录或进行文本挖掘。 在这项研究中，我们提出了一种无需人工标注即可自动提取和链接主诉中的实体的弱监督方法。首先，我们采用了分段匹配算法，在120万条经过去识别化且获得伦理委员会批准的真实世界主诉记录上生成弱标签，包括实体提及范围和类别标签。然后，我们使用基于BERT的模型和生成的弱标签来定位主诉文本中的实体，并将其链接到预定义的本体论中。 我们进行了广泛的实验，结果表明，在没有人工标注的情况下，我们的弱监督实体提取和链接（\ours）方法在性能上超过了以前的方法。
URL
https://arxiv.org/abs/2509.01899
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