Weakly_Supervised
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Analytical Logit Scaling for High-Resolution Sea Ice Topology Retrieval from Weakly Labeled SAR Imagery
2026-03-13 20:21:33Reda Elwaradi, Julien Gimenez, St\'ephane Hordoir, Mehdi Ait Hamma, Adrien Chan-Hon-Tong, Flora WeissgerberAbstract
High-resolution sea ice mapping using Synthetic Aperture Radar (SAR) is crucial for Arctic navigation and climate monitoring. However, operational ice charts provide only coarse, region-level polygons (weak labels), forcing automated segmentation models to struggle with pixel-level accuracy and often yielding under-confident, blurred concentration maps. In this paper, we propose a weakly supervised deep learning pipeline that fuses Sentinel-1 SAR and AMSR-2 radiometry data using a U-Net architecture trained with a region-based loss. To overcome the severe under-confidence caused by weak labels, we introduce an Analytical Logit Scaling method applied post-inference. By dynamically calculating the temperature and bias based on the latent space percentiles (2\% and 98\%) of each scene, we force a physical binarization of the predictions. This adaptive scaling acts as a topological extractor, successfully revealing fine-grained sea ice fractures (leads) at a 40-meter resolution without requiring any manual pixel-level annotations. Our approach not only resolves local topology but also perfectly preserves regional macroscopic concentrations, achieving a 78\% accuracy on highly fragmented summer scenes, thereby bridging the gap between weakly supervised learning and high-resolution physical segmentation.
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URL
https://arxiv.org/abs/2603.13573
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Adaptation of Weakly Supervised Localization in Histopathology by Debiasing Predictions
2026-03-12 21:34:21Alexis Guichemerre, Banafsheh Karimian, Soufiane Belharbi, Natacha Gillet, Nicolas Thome, Pourya Shamsolmoali, Mohammadhadi Shateri, Luke McCaffrey, Eric GrangerAbstract
Weakly Supervised Object Localization (WSOL) models enable joint classification and region-of-interest localization in histology images using only image-class supervision. When deployed in a target domain, distributions shift remains a major cause of performance degradation, especially when applied on new organs or institutions with different staining protocols and scanner characteristics. Under stronger cross-domain shifts, WSOL predictions can become biased toward dominant classes, producing highly skewed pseudo-label distributions in the target domain. Source-Free (Unsupervised) Domain Adaptation (SFDA) methods are commonly employed to address domain shift. However, because they rely on self-training, the initial bias is reinforced over training iterations, degrading both classification and localization tasks. We identify this amplification of prediction bias as a primary obstacle to the SFDA of WSOL models in histopathology. This paper introduces \sfdadep, a method inspired by machine unlearning that formulates SFDA as an iterative process of identifying and correcting prediction bias. It periodically identifies target images from over-predicted classes and selectively reduces the predictive confidence for uncertain (high entropy) images, while preserving confident predictions. This process reduces the drift of decision boundaries and bias toward dominant classes. A jointly optimized pixel-level classifier further restores discriminative localization features under distribution shift. Extensive experiments on cross-organ and -center histopathology benchmarks (glas, CAMELYON-16, CAMELYON-17) with several WSOL models show that SFDA-DeP consistently improves classification and localization over state-of-the-art SFDA baselines. {\small Code: \href{this https URL}{this http URL}}
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URL
https://arxiv.org/abs/2603.12468
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Enhancing Image Aesthetics with Dual-Conditioned Diffusion Models Guided by Multimodal Perception
2026-03-12 05:22:53Xinyu Nan, Ning Wang, Yuyao Zhai, Mei YangAbstract
Image aesthetic enhancement aims to perceive aesthetic deficiencies in images and perform corresponding editing operations, which is highly challenging and requires the model to possess creativity and aesthetic perception capabilities. Although recent advancements in image editing models have significantly enhanced their controllability and flexibility, they struggle with enhancing image aesthetic. The primary challenges are twofold: first, following editing instructions with aesthetic perception is difficult, and second, there is a scarcity of "perfectly-paired" images that have consistent content but distinct aesthetic qualities. In this paper, we propose Dual-supervised Image Aesthetic Enhancement (DIAE), a diffusion-based generative model with multimodal aesthetic perception. First, DIAE incorporates Multimodal Aesthetic Perception (MAP) to convert the ambiguous aesthetic instruction into explicit guidance by (i) employing detailed, standardized aesthetic instructions across multiple aesthetic attributes, and (ii) utilizing multimodal control signals derived from text-image pairs that maintain consistency within the same aesthetic attribute. Second, to mitigate the lack of "perfectly-paired" images, we collect "imperfectly-paired" dataset called IIAEData, consisting of images with varying aesthetic qualities while sharing identical semantics. To better leverage the weak matching characteristics of IIAEData during training, a dual-branch supervision framework is also introduced for weakly supervised image aesthetic enhancement. Experimental results demonstrate that DIAE outperforms the baselines and obtains superior image aesthetic scores and image content consistency scores.
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图像美学增强的目标是识别图片中的审美缺陷，并执行相应的编辑操作，这是一项极具挑战性的任务，要求模型具备创造力和审美感知能力。尽管最近的图像编辑模型在可控性和灵活性方面取得了显著进展，但它们仍难以提升图像的美感。主要面临的两个挑战是：首先，根据具有审美意识的指令进行编辑很困难；其次，“完美配对”的图像数据（即内容一致而美学特征不同的图像）稀缺。为此，在本文中我们提出了基于扩散模型的多模态审美感知图像美学增强方法(Dual-supervised Image Aesthetic Enhancement, DIAE)。 DIAE 方法主要包括两个方面： 1. **Multi-modal Aesthetic Perception (MAP)**：这种方法将模糊的审美指令转化为具体的指导信号，通过使用详细的、标准化的跨多个审美属性的指令，并利用从文本-图像对中提取出的一致于同一美学属性中的多模态控制信号。 2. 为了缓解“完美配对”数据稀缺的问题，我们收集了一个名为IIAEData的数据集，该数据集中包含了具有不同美学品质但语义一致性的图片。同时，为更好地利用IIAEData的弱匹配特性进行训练，我们还引入了一种双分支监督框架来实现弱监督图像美学增强。 实验结果表明，DIAE 方法不仅超越了基准方法，还在图像美学评分和图像内容一致性评分上取得了更好的成绩。
URL
https://arxiv.org/abs/2603.11556
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Weakly Supervised Teacher-Student Framework with Progressive Pseudo-mask Refinement for Gland Segmentation
2026-03-09 16:54:05Hikmat Khan, Wei Chen, Muhammad Khalid Khan NiaziAbstract
Background and objectives: Colorectal cancer histopathological grading depends on accurate segmentation of glandular structures. Current deep learning approaches rely on large scale pixel level annotations that are labor intensive and difficult to obtain in routine clinical practice. Weakly supervised semantic segmentation offers a promising alternative. However, class activation map based methods often produce incomplete pseudo masks that emphasize highly discriminative regions and fail to supervise unannotated glandular structures. We propose a weakly supervised teacher student framework that leverages sparse pathologist annotations and an Exponential Moving Average stabilized teacher network to generate refined pseudo masks. Methods: The framework integrates confidence based filtering, adaptive fusion of teacher predictions with limited ground truth, and curriculum guided refinement to progressively segment unannotated glandular regions. The method was evaluated on an institutional colorectal cancer cohort from The Ohio State University Wexner Medical Center consisting of 60 hematoxylin and eosin stained whole slide images and on public datasets including the Gland Segmentation dataset, TCGA COAD, TCGA READ, and SPIDER. Results: On the Gland Segmentation dataset the framework achieved a mean Intersection over Union of 80.10 and a mean Dice coefficient of 89.10. Cross cohort evaluation demonstrated robust generalization on TCGA COAD and TCGA READ without additional annotations, while reduced performance on SPIDER reflected domain shift. Conclusions: The proposed framework provides an annotation efficient and generalizable approach for gland segmentation in colorectal histopathology.
Abstract (translated)
背景与目标：结直肠癌的组织病理学分级依赖于腺体结构的精确分割。当前深度学习方法依赖于大规模像素级别的注释，这些注释在常规临床实践中耗时且难以获取。弱监督语义分割提供了一种有前景的替代方案。然而，基于类激活图的方法往往会产生不完整的伪掩模，过分强调具有高度区分性的区域，并且无法对未标注的腺体结构进行监督。我们提出了一种利用稀疏病理学家注释和指数移动平均稳定的教师网络生成精炼伪掩模的弱监督师生框架。 方法：该框架结合了基于置信度过滤、教师预测与有限真实标签自适应融合以及课程引导细化，逐步分割未标注的腺体区域。研究在来自俄亥俄州立大学韦克斯纳医学中心的一个机构结直肠癌队列上进行了评估，包括60张苏木精和伊红染色的全切片图像，并且还在公开数据集（如Gland Segmentation 数据集、TCGA COAD、TCGA READ 和 SPIDER）上进行了测试。 结果：在Gland Segmentation 数据集中，该框架实现了平均交并比为80.10%和平均Dice系数为89.10%。跨队列评估显示，在没有额外注释的情况下对TCGA COAD 和 TCGA READ 的泛化能力稳健，而SPIDER上的性能下降反映了域偏移。 结论：所提出的框架提供了一种注解效率高且具有广泛适用性的方法，用于结直肠组织病理学中的腺体分割。
URL
https://arxiv.org/abs/2603.08605
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Rewis3d: Reconstruction Improves Weakly-Supervised Semantic Segmentation
2026-03-06 15:25:20Jonas Ernst, Wolfgang Boettcher, Lukas Hoyer, Jan Eric Lenssen, Bernt SchieleAbstract
We present Rewis3d, a framework that leverages recent advances in feed-forward 3D reconstruction to significantly improve weakly supervised semantic segmentation on 2D images. Obtaining dense, pixel-level annotations remains a costly bottleneck for training segmentation models. Alleviating this issue, sparse annotations offer an efficient weakly-supervised alternative. However, they still incur a performance gap. To address this, we introduce a novel approach that leverages 3D scene reconstruction as an auxiliary supervisory signal. Our key insight is that 3D geometric structure recovered from 2D videos provides strong cues that can propagate sparse annotations across entire scenes. Specifically, a dual student-teacher architecture enforces semantic consistency between 2D images and reconstructed 3D point clouds, using state-of-the-art feed-forward reconstruction to generate reliable geometric supervision. Extensive experiments demonstrate that Rewis3d achieves state-of-the-art performance in sparse supervision, outperforming existing approaches by 2-7% without requiring additional labels or inference overhead.
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我们介绍了一个名为Rewis3d的框架，该框架利用近期在前馈式三维重建方面的进展，显著提升了二维图像上弱监督语义分割的效果。获得密集、像素级别的注释仍然是训练分割模型的一个昂贵瓶颈问题。为了解决这个问题，稀疏标注提供了一种有效的弱监督替代方案。然而，这种做法仍然会导致性能差距。为此，我们引入了一个新颖的方法，该方法利用三维场景重建作为辅助监督信号。 我们的关键见解是，从二维视频中恢复出的三维几何结构可以提供强大的线索，从而将稀疏注释传播到整个场景中。具体来说，一个双学生的教师架构强制执行二维图像和重构的三维点云之间的语义一致性，并使用最先进的前馈式重建来生成可靠的几何监督。 广泛的实验表明，Rewis3d在稀疏监督下达到了最先进的性能，相比现有方法提高了2-7%，并且不需要额外的标签或推理开销。
URL
https://arxiv.org/abs/2603.06374
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FreeOcc: Training-free Panoptic Occupancy Prediction via Foundation Models
2026-03-06 11:19:33Andrew Caunes, Thierry Chateau, Vincent FremontAbstract
Semantic and panoptic occupancy prediction for road scene analysis provides a dense 3D representation of the ego vehicle's surroundings. Current camera-only approaches typically rely on costly dense 3D supervision or require training models on data from the target domain, limiting deployment in unseen environments. We propose FreeOcc, a training-free pipeline that leverages pretrained foundation models to recover both semantics and geometry from multi-view images. FreeOcc extracts per-view panoptic priors with a promptable foundation segmentation model and prompt-to-taxonomy rules, and reconstructs metric 3D points with a reconstruction foundation model. Depth- and confidence- aware filtering lifts reliable labels into 3D, which are fused over time and voxelized with a deterministic refinement stack. For panoptic occupancy, instances are recovered by fitting and merging robust current-view 3D box candidates, enabling instance-aware occupancy without any learned 3D model. On Occ3D-nuScenes, FreeOcc achieves 16.9 mIoU and 16.5 RayIoU train-free, on par with state-of-the-art weakly supervised methods. When employed as a pseudo-label generation pipeline for training downstream models, it achieves 21.1 RayIoU, surpassing the previous state-of-the-art weakly supervised baseline. Furthermore, FreeOcc sets new baselines for both train-free and weakly supervised panoptic occupancy prediction, achieving 3.1 RayPQ and 3.9 RayPQ, respectively. These results highlight foundation-model-driven perception as a practical route to training-free 3D scene understanding.
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语义和全景占用预测对于道路场景分析提供了一个密集的3D环境表示，特别是针对自动驾驶车辆周围的环境。目前仅使用相机的方法通常依赖于昂贵的密集3D监督或需要在目标领域数据上训练模型，这限制了其在未知环境中部署的能力。我们提出了FreeOcc，这是一种无需训练的流水线方法，该方法利用预训练的基础模型从多视角图像中恢复语义和几何信息。FreeOcc通过使用可提示的基础分割模型和规则到分类法的映射来提取每视图的全景先验，并且通过重建基础模型来重构度量3D点。深度感知和置信度感知过滤将可靠的标签提升至三维空间，随后这些标签被时间融合并用确定性细化堆栈进行体素化处理。对于全景占用预测，FreeOcc通过拟合和合并稳健的当前视图3D边界框候选来恢复实例信息，从而在不依赖任何学习到的3D模型的情况下实现基于实例的占用预测。 在Occ3D-nuScenes数据集上，FreeOcc实现了16.9 mIoU（平均交并比）和16.5 RayIoU（光线交并比），这与现有的弱监督方法的表现相当。当作为下游模型训练中的伪标签生成流程使用时，它可以达到21.1 RayIoU的性能水平，超过了之前最先进的弱监督基线。此外，FreeOcc为无训练和弱监督全景占用预测分别设定了新的基准，分别是3.1 RayPQ（光线交并质量）和3.9 RayPQ。 这些结果突显了基于基础模型驱动感知作为实现无需训练的3D场景理解的一种实用途径的重要性。
URL
https://arxiv.org/abs/2603.06166
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Towards Driver Behavior Understanding: Weakly-Supervised Risk Perception in Driving Scenes
2026-03-06 05:40:13Nakul Agarwal, Yi-Ting Chen, Behzad DariushAbstract
Achieving zero-collision mobility remains a key objective for intelligent vehicle systems, which requires understanding driver risk perception-a complex cognitive process shaped by voluntary response of the driver to external stimuli and the attentiveness of surrounding road users towards the ego-vehicle. To support progress in this area, we introduce RAID (Risk Assessment In Driving scenes)-a large-scale dataset specifically curated for research on driver risk perception and contextual risk assessment. RAID comprises 4,691 annotated video clips, covering diverse traffic scenarios with labels for driver's intended maneuver, road topology, risk situations (e.g., crossing pedestrians), driver responses, and pedestrian attentiveness. Leveraging RAID, we propose a weakly supervised risk object identification framework that models the relationship between driver's intended maneuver and responses to identify potential risk sources. Additionally, we analyze the role of pedestrian attention in estimating risk and demonstrate the value of the proposed dataset. Experimental evaluations demonstrate that our method achieves 20.6% and 23.1% performance gains over prior state-of-the-art approaches on the RAID and HDDS datasets, respectively.
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实现零碰撞的机动性仍然是智能车辆系统的关键目标，这需要理解驾驶员的风险感知——这是一种复杂的认知过程，由驾驶员对外部刺激的自愿反应和周围道路使用者对自身车辆的关注程度所塑造。为了推动这一领域的进步，我们引入了RAID（驾驶场景中的风险评估）——一个专门用于研究驾驶员风险感知和情境风险评估的大规模数据集。RAID包含4,691个注释视频片段，涵盖了各种交通场景，并为驾驶员的意图操作、道路拓扑结构、风险情况（如过马路行人）、驾驶员反应以及行人的注意程度等提供了标签。 利用RAID，我们提出了一种弱监督的风险对象识别框架，该框架通过建模驾驶员意图操作和响应之间的关系来识别潜在的风险来源。此外，我们还分析了行人注意力在评估风险中的作用，并展示了所提议数据集的价值。实验评估表明，在RAID和HDDS数据集上，我们的方法分别比现有的最先进的方法提高了20.6%和23.1%的性能。
URL
https://arxiv.org/abs/2603.05926
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GEM-TFL: Bridging Weak and Full Supervision for Forgery Localization through EM-Guided Decomposition and Temporal Refinement
2026-03-05 12:07:26Xiaodong Zhu, Yuanming Zheng, Suting Wang, Junqi Yang, Yuhong Yang, Weiping Tu, Zhongyuan WangAbstract
Temporal Forgery Localization (TFL) aims to precisely identify manipulated segments within videos or audio streams, providing interpretable evidence for multimedia forensics and security. While most existing TFL methods rely on dense frame-level labels in a fully supervised manner, Weakly Supervised TFL (WS-TFL) reduces labeling cost by learning only from binary video-level labels. However, current WS-TFL approaches suffer from mismatched training and inference objectives, limited supervision from binary labels, gradient blockage caused by non-differentiable top-k aggregation, and the absence of explicit modeling of inter-proposal relationships. To address these issues, we propose GEM-TFL (Graph-based EM-powered Temporal Forgery Localization), a two-phase classification-regression framework that effectively bridges the supervision gap between training and inference. Built upon this foundation, (1) we enhance weak supervision by reformulating binary labels into multi-dimensional latent attributes through an EM-based optimization process; (2) we introduce a training-free temporal consistency refinement that realigns frame-level predictions for smoother temporal dynamics; and (3) we design a graph-based proposal refinement module that models temporal-semantic relationships among proposals for globally consistent confidence estimation. Extensive experiments on benchmark datasets demonstrate that GEM-TFL achieves more accurate and robust temporal forgery localization, substantially narrowing the gap with fully supervised methods.
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时间伪造定位（TFL）旨在精确识别视频或音频流中的篡改片段，为多媒体取证和安全提供可解释的证据。虽然大多数现有的TFL方法依赖于密集的关键帧标签进行全面监督学习，弱监督时间伪造定位（WS-TFL）通过仅从二进制级视频标签中学习来降低标注成本。然而，当前的WS-TFL方法面临着训练与推理目标不匹配、来自二元标签的有限指导、由非可微分top-k聚合引起的梯度阻塞以及缺乏显式的提议间关系建模的问题。 为了解决这些问题，我们提出了GEM-TFL（基于图和期望最大化的时间伪造定位），这是一种两阶段分类-回归框架，有效地弥合了训练与推理之间的监督差距。在此基础上： 1. 我们通过基于EM的优化过程将二进制标签重构成多维潜在属性，以增强弱监督； 2. 我们引入了一种无需训练的时间一致性细化方法，重新对齐帧级预测以获得更平滑的时间动态变化； 3. 我们设计了一个图基提议细化模块，通过建模提议之间的时空关系来进行全局一致的置信度估计。 在基准数据集上的广泛实验表明，GEM-TFL实现了更加准确和鲁棒的时间伪造定位，并且大大缩小了与全监督方法之间的差距。
URL
https://arxiv.org/abs/2603.05095
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Mitigating Instance Entanglement in Instance-Dependent Partial Label Learning
2026-03-05 05:19:12Rui Zhao, Bin Shi, Kai Sun, Bo DongAbstract
Partial label learning is a prominent weakly supervised classification task, where each training instance is ambiguously labeled with a set of candidate labels. In real-world scenarios, candidate labels are often influenced by instance features, leading to the emergence of instance-dependent PLL (ID-PLL), a setting that more accurately reflects this relationship. A significant challenge in ID-PLL is instance entanglement, where instances from similar classes share overlapping features and candidate labels, resulting in increased class confusion. To address this issue, we propose a novel Class-specific Augmentation based Disentanglement (CAD) framework, which tackles instance entanglement by both intra- and inter-class regulations. For intra-class regulation, CAD amplifies class-specific features to generate class-wise augmentations and aligns same-class augmentations across instances. For inter-class regulation, CAD introduces a weighted penalty loss function that applies stronger penalties to more ambiguous labels, encouraging larger inter-class distances. By jointly applying intra- and inter-class regulations, CAD improves the clarity of class boundaries and reduces class confusion caused by entanglement. Extensive experimental results demonstrate the effectiveness of CAD in mitigating the entanglement problem and enhancing ID-PLL performance. The code is available at this https URL.
Abstract (translated)
部分标签学习是一种突出的弱监督分类任务，其中每个训练样本都含糊地标记有一组候选标签。在实际场景中，候选标签通常会受到实例特征的影响，从而导致实例依赖型部分标签学习（ID-PLL）的出现，这种设定更准确地反映了两者之间的关系。ID-PLL的一个重要挑战是实例纠缠，即来自相似类别的实例共享重叠的特征和候选标签，这增加了类别混淆的可能性。为了解决这一问题，我们提出了一种基于特定类别的增强去纠缠框架（CAD），该框架通过类内和类间调节来解决实例纠缠问题。 对于类内调节，CAD放大了每个类别的特有特征以生成针对不同类别的数据增强，并对同一类别中的各个实例进行调整以确保一致性。在类间调节方面，CAD引入了一个加权惩罚损失函数，该函数对更加模糊的标签施加强烈的惩罚，鼓励更大的类间距。 通过同时应用类内和类间的调节机制，CAD框架能够提高类别边界的清晰度，并减少由于纠缠而导致的类别混淆问题。大量的实验结果表明，CAD在缓解纠缠问题以及提升ID-PLL性能方面具有显著效果。代码可在以下链接获取：[此处插入实际的URL地址]。
URL
https://arxiv.org/abs/2603.04825
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Weakly Supervised Patch Annotation for Improved Screening of Diabetic Retinopathy
2026-03-04 12:35:55Shramana Dey, Abhirup Banerjee, B. Uma Shankar, Ramachandran Rajalakshmi, Sushmita MitraAbstract
Diabetic Retinopathy (DR) requires timely screening to prevent irreversible vision loss. However, its early detection remains a significant challenge since often the subtle pathological manifestations (lesions) get overlooked due to insufficient annotation. Existing literature primarily focuses on image-level supervision, weakly-supervised localization, and clustering-based representation learning, which fail to systematically annotate unlabeled lesion region(s) for refining the dataset. Expert-driven lesion annotation is labor-intensive and often incomplete, limiting the performance of deep learning models. We introduce Similarity-based Annotation via Feature-space Ensemble (SAFE), a two-stage framework that unifies weak supervision, contrastive learning, and patch-wise embedding inference, to systematically expand sparse annotations in the pathology. SAFE preserves fine-grained details of the lesion(s) under partial clinical supervision. In the first stage, a dual-arm Patch Embedding Network learns semantically structured, class-discriminative embeddings from expert annotated patches. Next, an ensemble of independent embedding spaces extrapolates labels to the unannotated regions based on spatial and semantic proximity. An abstention mechanism ensures trade-off between highly reliable annotation and noisy coverage. Experimental results demonstrate reliable separation of healthy and diseased patches, achieving upto 0.9886 accuracy. The annotation generated from SAFE substantially improves downstream tasks such as DR classification, demonstrating a substantial increase in F1-score of the diseased class and a performance gain as high as 0.545 in Area Under the Precision-Recall Curve (AUPRC). Qualitative analysis, with explainability, confirms that SAFE focuses on clinically relevant lesion patterns; and is further validated by ophthalmologists.
Abstract (translated)
糖尿病视网膜病变（DR）需要及时筛查以防止不可逆的视力丧失。然而，其早期检测仍然是一个重大挑战，因为细微的病理表现（病灶）常常由于标注不足而被忽视。现有文献主要集中在图像级监督、弱监督定位和基于聚类表示学习上，这些方法未能系统地为未标记病变区域进行注释以优化数据集。专家驱动的病变标注耗时且通常不完整，限制了深度学习模型的表现。我们引入了一种名为通过特征空间集成实现相似性标注（SAFE）的方法，这是一种两阶段框架，将弱监督、对比学习和基于补丁嵌入推断统一起来，在病理学中系统地扩展稀疏注释。 SAFE在部分临床监护下保留病变的细节信息。在第一阶段，双臂Patch Embedding Network从专家标记的补丁中学习语义结构化且区分度高的嵌入表示。接下来，一组独立的嵌入空间基于空间和语义邻近性将标签外推到未标注区域。一个弃权机制确保了高可靠注释与噪声覆盖率之间的平衡。实验结果表明，SAFE能够可靠地区分健康和病变补丁，并达到了高达0.9886的准确率。来自SAFE生成的标注极大地改善了下游任务如DR分类的表现，患病类别的F1分数显著提高，以及在精度-召回曲线下的面积（AUPRC）提高了最多达0.545。基于可解释性的定性分析证实了SAFE专注于临床相关的病变模式，并且这一方法还得到了眼科医生的验证和认可。
URL
https://arxiv.org/abs/2603.03991
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Geographically-Weighted Weakly Supervised Bayesian High-Resolution Transformer for 200m Resolution Pan-Arctic Sea Ice Concentration Mapping and Uncertainty Estimation using Sentinel-1, RCM, and AMSR2 Data
2026-03-03 20:23:11Mabel Heffring, Lincoln Linlin XuAbstract
Although high-resolution mapping of pan-Arctic sea ice with reliable corresponding uncertainty is essential for operational sea ice concentration (SIC) charting, it is a difficult task due to key challenges, such as the subtle nature of ice signature features, inexact SIC labels, model uncertainty, and data heterogeneity. This study presents a novel Bayesian High-Resolution Transformer approach for 200 meter resolution pan-Arctic SIC mapping and uncertainty quantification using Sentinel-1, RADARSAT Constellation Mission (RCM), and Advanced Microwave Scanning Radiometer 2 (AMSR2) data. First, to improve small and subtle sea ice feature (e.g., cracks/leads, ponds, and ice floes) extraction, we design a novel high-resolution Transformer model with both global and local modules that can better discern the subtle differences in sea ice patterns. Second, to address low-resolution and inexact SIC labels, we design a geographically-weighted weakly supervised loss function to supervise the model at region level instead of pixel level, and to prioritize pure open water and ice pack signatures while mitigating the impact of ambiguity in the marginal ice zone (MIZ). Third, to improve uncertainty quantification, we design a Bayesian extension of the proposed Transformer model, treating its parameters as random variables to more effectively capture uncertainties. Fourth, to address data heterogeneity, we fuse three different data types (Sentinel-1, RCM, and AMSR2) at decision-level to improve both SIC mapping and uncertainty quantification. The proposed approach is evaluated under pan-Arctic minimum-extent conditions in 2021 and 2025. Results demonstrate that the proposed model achieves 0.70 overall feature detection accuracy using Sentinel-1 data, while also preserving pan-Arctic SIC patterns (Sentinel-1 R\textsuperscript{2} = 0.90 relative to the ARTIST Sea Ice product).
Abstract (translated)
尽管高分辨率的泛北极海冰制图并可靠地量化相应的不确定性对于操作性海冰浓度（SIC）绘图至关重要，但由于一些关键挑战——如冰特征细微、SIC标签不准确、模型不确定性以及数据异质性的原因，这一任务十分艰难。本研究提出了一种新颖的贝叶斯高分辨率变换器方法，用于利用Sentinel-1、RADARSAT星座使命（RCM）和高级微波扫描辐射计2号（AMSR2）的数据进行泛北极海冰浓度的200米分辨率制图及不确定性量化。具体而言： 第一，为了改进小而微妙的海冰特征（如裂缝/缝隙、池塘、浮冰）的提取，我们设计了一种新颖的高分辨率变换器模型，该模型同时具备全局和局部模块，能够更好地识别海冰模式中的细微差异。 第二，为了解决低分辨率和不准确的SIC标签问题，我们设计了一个地理加权弱监督损失函数，在区域级别而非像素级别上监督模型，并优先考虑纯净开放水域及密集冰区的特征，同时缓解边际冰带（MIZ）中模糊性的不利影响。 第三，为了改进不确定性量化，我们将贝叶斯扩展应用于提出的变换器模型，将其参数视为随机变量，从而更有效地捕捉到不确定因素。 第四，为了解决数据异质性问题，我们融合了三种不同类型的数据（Sentinel-1、RCM和AMSR2），在决策层面提高SIC制图及不确定性量化的准确性。 所提出的方法已在2021年和2025年的泛北极最小范围条件下进行了评估。结果显示，在使用Sentinel-1数据的情况下，该模型实现了0.70的整体特征检测准确率，并且保留了泛北极的海冰浓度模式（相对于ARTIST Sea Ice产品，Sentinel-1 R² = 0.90）。
URL
https://arxiv.org/abs/2603.03503
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Benchmarking Compact VLMs for Clip-Level Surveillance Anomaly Detection Under Weak Supervision
2026-03-03 11:42:47Kirill Borodin, Kirill Kondrashov, Nikita Vasiliev, Ksenia Gladkova, Inna Larina, Mikhail Gorodnichev, Grach MkrtchianAbstract
CCTV safety monitoring demands anomaly detectors combine reliable clip-level accuracy with predictable per-clip latency despite weak supervision. This work investigates compact vision-language models (VLMs) as practical detectors for this regime. A unified evaluation protocol standardizes preprocessing, prompting, dataset splits, metrics, and runtime settings to compare parameter-efficiently adapted compact VLMs against training-free VLM pipelines and weakly supervised baselines. Evaluation spans accuracy, precision, recall, F1, ROC-AUC, and average per-clip latency to jointly quantify detection quality and efficiency. With parameter-efficient adaptation, compact VLMs achieve performance on par with, and in several cases exceeding, established approaches while retaining competitive per-clip latency. Adaptation further reduces prompt sensitivity, producing more consistent behavior across prompt regimes under the shared protocol. These results show that parameter-efficient fine-tuning enables compact VLMs to serve as dependable clip-level anomaly detectors, yielding a favorable accuracy-efficiency trade-off within a transparent and consistent experimental setup.
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URL
https://arxiv.org/abs/2603.13306
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Weakly Supervised Video Anomaly Detection with Anomaly-Connected Components and Intention Reasoning
Abstract
Weakly supervised video anomaly detection (WS-VAD) involves identifying the temporal intervals that contain anomalous events in untrimmed videos, where only video-level annotations are provided as supervisory signals. However, a key limitation persists in WS-VAD, as dense frame-level annotations are absent, which often leaves existing methods struggling to learn anomaly semantics effectively. To address this issue, we propose a novel framework named LAS-VAD, short for Learning Anomaly Semantics for WS-VAD, which integrates anomaly-connected component mechanism and intention awareness mechanism. The former is designed to assign video frames into distinct semantic groups within a video, and frame segments within the same group are deemed to share identical semantic information. The latter leverages an intention-aware strategy to distinguish between similar normal and abnormal behaviors (e.g., taking items and stealing). To further model the semantic information of anomalies, as anomaly occurrence is accompanied by distinct characteristic attributes (i.e., explosions are characterized by flames and thick smoke), we additionally incorporate anomaly attribute information to guide accurate detection. Extensive experiments on two benchmark datasets, XD-Violence and UCF-Crime, demonstrate that our LAS-VAD outperforms current state-of-the-art methods with remarkable gains.
Abstract (translated)
弱监督视频异常检测（WS-VAD）旨在识别未修剪视频中包含异常事件的时间间隔，仅使用视频级别的标注作为监督信号。然而，在这种情况下，由于缺少密集的帧级注释，现有的方法在有效学习异常语义方面面临挑战。为了解决这个问题，我们提出了一种新的框架——用于弱监督视频异常检测（WS-VAD）的学习异常语义框架（LAS-VAD），该框架结合了异常连接组件机制和意图感知机制。 异常连接组件机制旨在将视频帧分配到视频内的不同语义组中，并认为同一组中的帧片段共享相同的语义信息。而意图感知策略则通过识别类似但不同的正常行为与异常行为（例如，取物品和偷窃）之间的区别来提高检测准确性。 此外，为了进一步建模异常的语义信息——因为异常发生时伴随着独特的特征属性（如爆炸事件具有火焰和浓烟等特定属性），我们还整合了异常属性信息以指导准确检测。在两个基准数据集XD-Violence和UCF-Crime上进行的大量实验表明，我们的LAS-VAD方法比当前最先进的方法有显著的性能提升。
URL
https://arxiv.org/abs/2603.00550
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FastBUS: A Fast Bayesian Framework for Unified Weakly-Supervised Learning
2026-02-28 07:27:21Ziquan Wang, Haobo Wang, Ke Chen, Lei Feng, Gang ChenAbstract
Machine Learning often involves various imprecise labels, leading to diverse weakly supervised settings. While recent methods aim for universal handling, they usually suffer from complex manual pre-work, ignore the relationships between associated labels, or are unable to batch process due to computational design flaws, resulting in long running times. To address these limitations, we propose a novel general framework that efficiently infers latent true label distributions across various weak supervisions. Our key idea is to express the label brute-force search process as a probabilistic transition of label variables, compressing diverse weakly supervised DFS tree structures into a shared Bayesian network. From this, we derived a latent probability calculation algorithm based on generalized belief propagation and proposed two joint acceleration strategies: 1) introducing a low-rank assumption to approximate the transition matrix, reducing time complexity; 2) designing an end-to-end state evolution module to learn batch-scale transition matrices, facilitating multi-category batch processing. In addition, the equivalence of our method with the EM algorithm in most scenarios is further demonstrated. Extensive experiments show that our method achieves SOTA results under most weakly supervised settings, and achieves up to hundreds of times faster acceleration in running time compared to other general methods.
Abstract (translated)
机器学习经常涉及各种不精确的标签，导致了多种弱监督设置。尽管最近的方法旨在实现通用处理，但它们通常需要复杂的手动前期工作、忽略相关标签之间的关系，或者由于计算设计缺陷而无法批量处理，从而导致运行时间过长。为了解决这些限制，我们提出了一种新的通用框架，该框架能够高效地在各种弱监督下推断潜在的真实标签分布。 我们的核心思想是将标签暴力搜索过程表达为标签变量的概率转换，并将多样的弱监督DFS（深度优先搜索）树结构压缩成一个共享的贝叶斯网络。基于此，我们从广义信念传播中导出了一个潜在概率计算算法，并提出了两种联合加速策略： 1. 引入低秩假设来近似转移矩阵，从而降低时间复杂度； 2. 设计了一个端到端状态演化模块以学习批量规模的转移矩阵，促进多类别批量处理。 此外，我们的方法在大多数情况下与EM（期望最大化）算法等价性也得到了进一步证明。广泛的实验表明，在大多数弱监督设置下，我们的方法实现了最先进的结果，并且相比其他通用方法能够实现运行时间最多快几百倍的加速效果。
URL
https://arxiv.org/abs/2603.00517
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TriLite: Efficient Weakly Supervised Object Localization with Universal Visual Features and Tri-Region Disentanglement
2026-02-26 15:33:22Arian Sabaghi, Jos\'e OramasAbstract
Weakly supervised object localization (WSOL) aims to localize target objects in images using only image-level labels. Despite recent progress, many approaches still rely on multi-stage pipelines or full fine-tuning of large backbones, which increases training cost, while the broader WSOL community continues to face the challenge of partial object coverage. We present TriLite, a single-stage WSOL framework that leverages a frozen Vision Transformer with Dinov2 pre-training in a self-supervised manner, and introduces only a minimal number of trainable parameters (fewer than 800K on ImageNet-1K) for both classification and localization. At its core is the proposed TriHead module, which decomposes patch features into foreground, background, and ambiguous regions, thereby improving object coverage while suppressing spurious activations. By disentangling classification and localization objectives, TriLite effectively exploits the universal representations learned by self-supervised ViTs without requiring expensive end-to-end training. Extensive experiments on CUB-200-2011, ImageNet-1K, and OpenImages demonstrate that TriLite sets a new state of the art, while remaining significantly more parameter-efficient and easier to train than prior methods. The code will be released soon.
Abstract (translated)
弱监督目标定位（WSOL）旨在仅使用图像级标签来识别图像中的目标对象。尽管近年来取得了一些进展，但许多方法仍然依赖于多阶段流程或大型骨干网络的完全微调，这增加了训练成本，而更广泛的WSOL社区仍在面对部分目标覆盖范围的问题挑战。 我们提出了一种名为TriLite的一阶段WSOL框架，该框架利用冻结的Vision Transformer（ViT）在自监督模式下进行Dinov2预训练，并且引入了非常少量可训练参数（少于800K个参数用于ImageNet-1K），这些参数同时支持分类和定位任务。TriLite的核心是所提出的TriHead模块，该模块将图像块特征分解为前景、背景及模棱两可区域，从而提高目标覆盖范围并抑制不必要激活。 通过分离分类和定位的目标，TriLite可以有效地利用自监督ViT学习到的通用表示，而无需昂贵的端到端训练。在CUB-200-2011、ImageNet-1K 和 OpenImages 上进行的广泛实验表明，TriLite在保持比先前方法更高效且更容易训练的同时，确立了新的技术水平。代码不久将公开发布。
URL
https://arxiv.org/abs/2602.23120
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Cytoarchitecture in Words: Weakly Supervised Vision-Language Modeling for Human Brain Microscopy
2026-02-26 15:10:39Matthew Sutton, Katrin Amunts, Timo Dickscheid, Christian SchifferAbstract
Foundation models increasingly offer potential to support interactive, agentic workflows that assist researchers during analysis and interpretation of image data. Such workflows often require coupling vision to language to provide a natural-language interface. However, paired image-text data needed to learn this coupling are scarce and difficult to obtain in many research and clinical settings. One such setting is microscopic analysis of cell-body-stained histological human brain sections, which enables the study of cytoarchitecture: cell density and morphology and their laminar and areal organization. Here, we propose a label-mediated method that generates meaningful captions from images by linking images and text only through a label, without requiring curated paired image-text data. Given the label, we automatically mine area descriptions from related literature and use them as synthetic captions reflecting canonical cytoarchitectonic attributes. An existing cytoarchitectonic vision foundation model (CytoNet) is then coupled to a large language model via an image-to-text training objective, enabling microscopy regions to be described in natural language. Across 57 brain areas, the resulting method produces plausible area-level descriptions and supports open-set use through explicit rejection of unseen areas. It matches the cytoarchitectonic reference label for in-scope patches with 90.6% accuracy and, with the area label masked, its descriptions remain discriminative enough to recover the area in an 8-way test with 68.6% accuracy. These results suggest that weak, label-mediated pairing can suffice to connect existing biomedical vision foundation models to language, providing a practical recipe for integrating natural-language in domains where fine-grained paired annotations are scarce.
Abstract (translated)
基础模型日益展现出支持互动、代理工作流程的潜力，这些工作流程能在图像数据分析过程中协助研究人员。这类工作流程通常需要将视觉与语言相结合，以提供自然语言界面。然而，在许多研究和临床环境中获取学习这种结合所需的配对图像文本数据却非常稀缺且难以获得。一个典型的例子是人类大脑切片中细胞体着色组织学的显微分析，这有助于研究细胞结构：包括细胞密度、形态以及它们在层状和区域中的排列方式。在此背景下，我们提出了一种标签介导的方法，该方法通过标签将图像与文本链接起来生成有意义的描述，而无需依赖精心策划的配对图像文本数据。给定特定标签后，我们可以自动从相关文献中挖掘出对应区域的描述，并将其用作合成说明，反映规范化的细胞结构属性。然后，现有的细胞结构视觉基础模型（CytoNet）通过一个将图像转换为文本的训练目标与大型语言模型相结合，使得显微镜下观察到的区域能够以自然语言进行描述。在涉及57个脑区的情况下，该方法产生的描述符具备了合理的区域性特征，并支持开放集使用，通过对未见过的区域明确拒绝来实现这一点。对于在范围内的补丁，其匹配细胞结构参考标签准确率为90.6%，并且即使遮蔽了区域标签，在一个八分类测试中仍能以68.6%的准确率恢复出该区域。 这些结果表明，弱标签介导配对足以将现有的生物医学视觉基础模型与语言连接起来，在精细标注稀缺的情况下为自然语言的集成提供了一个实际的方法。
URL
https://arxiv.org/abs/2602.23088
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Cross Pseudo Labeling For Weakly Supervised Video Anomaly Detection
2026-02-19 04:42:13Lee Dayeon, Kim Dongheyong, Park Chaewon, Woo Sungmin, Lee SangyounAbstract
Weakly supervised video anomaly detection aims to detect anomalies and identify abnormal categories with only video-level labels. We propose CPL-VAD, a dual-branch framework with cross pseudo labeling. The binary anomaly detection branch focuses on snippet-level anomaly localization, while the category classification branch leverages vision-language alignment to recognize abnormal event categories. By exchanging pseudo labels, the two branches transfer complementary strengths, combining temporal precision with semantic discrimination. Experiments on XD-Violence and UCF-Crime demonstrate that CPL-VAD achieves state-of-the-art performance in both anomaly detection and abnormal category classification.
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URL
https://arxiv.org/abs/2602.17077
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MB-DSMIL-CL-PL: Scalable Weakly Supervised Ovarian Cancer Subtype Classification and Localisation Using Contrastive and Prototype Learning with Frozen Patch Features
2026-02-16 19:33:33Marcus Jenkins, Jasenka Mazibrada, Bogdan Leahu, Michal MackiewiczAbstract
The study of histopathological subtypes is valuable for the personalisation of effective treatment strategies for ovarian cancer. However, increasing diagnostic workloads present a challenge for UK pathology departments, leading to the rise in AI approaches. While traditional approaches in this field have relied on pre-computed, frozen image features, recent advances have shifted towards end-to-end feature extraction, providing an improvement in accuracy but at the expense of significantly reduced scalability during training and time-consuming experimentation. In this paper, we propose a new approach for subtype classification and localisation in ovarian cancer histopathology images using contrastive and prototype learning with pre-computed, frozen features via feature-space augmentations. Compared to DSMIL, our method achieves an improvement of 70.4\% and 15.3\% in F1 score for instance- and slide-level classification, respectively, along with AUC gains of 16.9\% for instance localisation and 2.3\% for slide classification, while maintaining the use of frozen patch features.
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URL
https://arxiv.org/abs/2602.15138
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AIWizards at MULTIPRIDE: A Hierarchical Approach to Slur Reclamation Detection
2026-02-13 11:01:19Luca Tedeschini, Matteo FasuloAbstract
Detecting reclaimed slurs represents a fundamental challenge for hate speech detection systems, as the same lexcal items can function either as abusive expressions or as in-group affirmations depending on social identity and context. In this work, we address Subtask B of the MultiPRIDE shared task at EVALITA 2026 by proposing a hierarchical approach to modeling the slur reclamation process. Our core assumption is that members of the LGBTQ+ community are more likely, on average, to employ certain slurs in a eclamatory manner. Based on this hypothesis, we decompose the task into two stages. First, using a weakly supervised LLM-based annotation, we assign fuzzy labels to users indicating the likelihood of belonging to the LGBTQ+ community, inferred from the tweet and the user bio. These soft labels are then used to train a BERT-like model to predict community membership, encouraging the model to learn latent representations associated with LGBTQ+ identity. In the second stage, we integrate this latent space with a newly initialized model for the downstream slur reclamation detection task. The intuition is that the first model encodes user-oriented sociolinguistic signals, which are then fused with representations learned by a model pretrained for hate speech detection. Experimental results on Italian and Spanish show that our approach achieves performance statistically comparable to a strong BERT-based baseline, while providing a modular and extensible framework for incorporating sociolinguistic context into hate speech modeling. We argue that more fine-grained hierarchical modeling of user identity and discourse context may further improve the detection of reclaimed language. We release our code at this https URL.
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URL
https://arxiv.org/abs/2602.12818
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Prototype-driven fusion of pathology and spatial transcriptomics for interpretable survival prediction
2026-02-12 21:59:07Lihe Liu, Xiaoxi Pan, Yinyin Yuan, Lulu ShangAbstract
Whole slide images (WSIs) enable weakly supervised prognostic modeling via multiple instance learning (MIL). Spatial transcriptomics (ST) preserves in situ gene expression, providing a spatial molecular context that complements morphology. As paired WSI-ST cohorts scale to population level, leveraging their complementary spatial signals for prognosis becomes crucial; however, principled cross-modal fusion strategies remain limited for this paradigm. To this end, we introduce PathoSpatial, an interpretable end-to-end framework integrating co-registered WSIs and ST to learn spatially informed prognostic representations. PathoSpatial uses task-guided prototype learning within a multi-level experts architecture, adaptively orchestrating unsupervised within-modality discovery with supervised cross-modal aggregation. By design, PathoSpatial substantially strengthens interpretability while maintaining discriminative ability. We evaluate PathoSpatial on a triple-negative breast cancer cohort with paired ST and WSIs. PathoSpatial delivers strong and consistent performance across five survival endpoints, achieving superior or comparable performance to leading unimodal and multimodal methods. PathoSpatial inherently enables post-hoc prototype interpretation and molecular risk decomposition, providing quantitative, biologically grounded explanations, highlighting candidate prognostic factors. We present PathoSpatial as a proof-of-concept for scalable and interpretable multimodal learning for spatial omics-pathology fusion.
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URL
https://arxiv.org/abs/2602.12441
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