Weakly_Supervised
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OpenDPR: Open-Vocabulary Change Detection via Vision-Centric Diffusion-Guided Prototype Retrieval for Remote Sensing Imagery
2026-03-29 11:43:02Qi Guo, Jue Wang, Yinhe Liu, Yanfei ZhongAbstract
Open-vocabulary change detection (OVCD) seeks to recognize arbitrary changes of interest by enabling generalization beyond a fixed set of predefined classes. We reformulate OVCD as a two-stage pipeline: first generate class-agnostic change proposals using visual foundation models (VFMs) such as SAM and DINOv2, and then perform category identification with vision-language models (VLMs) such as CLIP. We reveal that category identification errors are the primary bottleneck of OVCD, mainly due to the limited ability of VLMs based on image-text matching to represent fine-grained land-cover categories. To address this, we propose OpenDPR, a training-free vision-centric diffusion-guided prototype retrieval framework. OpenDPR leverages diffusion models to construct diverse prototypes for target categories offline, and to perform similarity retrieval with change proposals in the visual space during inference. The secondary bottleneck lies in change localization, due to the inherent lack of change priors in VFMs. To bridge this gap, we design a spatial-to-change weakly supervised change detection module named S2C to adapt their strong spatial modeling capabilities for change localization. Integrating the pretrained S2C into OpenDPR leads to an optional weakly supervised variant named OpenDPR-W, which further improves OVCD with minimal supervision. Experimental results on four benchmark datasets demonstrate that the proposed methods achieve state-of-the-art performance under both supervision modes. Code is available at this https URL.
Abstract (translated)
开放词汇变化检测（OVCD）旨在通过超越预定义固定类别的泛化能力，识别任意感兴趣的变化。我们将OVCD重构为两阶段流程：首先利用SAM和DINOv2等视觉基础模型（VFMs）生成与类别无关的变化提案，随后通过CLIP等视觉-语言模型（VLMs）进行类别识别。研究发现，类别识别错误是OVCD的主要瓶颈，这主要源于基于图像-文本匹配的VLM在表征细粒度土地覆盖类别方面的能力有限。为此，我们提出OpenDPR——一个无需训练的、以视觉为中心的扩散引导原型检索框架。OpenDPR利用扩散模型离线构建目标类别的多样化原型，并在推理阶段在视觉空间与变化提案进行相似性检索。次要瓶颈在于变化定位，这源于VFMs本身缺乏变化先验知识。为弥合这一差距，我们设计了名为S2C的空间到变化弱监督变化检测模块，使其强大的空间建模能力适配变化定位任务。将预训练的S2C集成到OpenDPR中，形成了可选的弱监督变体OpenDPR-W，该变体通过最小监督进一步提升了OVCD性能。在四个基准数据集上的实验结果表明，所提方法在两种监督模式下均达到最先进性能。代码已公开于此链接。
URL
https://arxiv.org/abs/2603.27645
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MLE-UVAD: Minimal Latent Entropy Autoencoder for Fully Unsupervised Video Anomaly Detection
2026-03-25 02:51:26Yuang Geng, Junkai Zhou, Kang Yang, Pan He, Zhuoyang Zhou, Jose C. Principe, Joel Harley, Ivan RuchkinAbstract
In this paper, we address the challenging problem of single-scene, fully unsupervised video anomaly detection (VAD), where raw videos containing both normal and abnormal events are used directly for training and testing without any labels. This differs sharply from prior work that either requires extensive labeling (fully or weakly supervised) or depends on normal-only videos (one-class classification), which are vulnerable to distribution shifts and contamination. We propose an entropy-guided autoencoder that detects anomalies through reconstruction error by reconstructing normal frames well while making anomalies reconstruct poorly. The key idea is to combine the standard reconstruction loss with a novel Minimal Latent Entropy (MLE) loss in the autoencoder. Reconstruction loss alone maps normal and abnormal inputs to distinct latent clusters due to their inherent differences, but also risks reconstructing anomalies too well to detect. Therefore, MLE loss addresses this by minimizing the entropy of latent embeddings, encouraging them to concentrate around high-density regions. Since normal frames dominate the raw video, sparse anomalous embeddings are pulled into the normal cluster, so the decoder emphasizes normal patterns and produces poor reconstructions for anomalies. This dual-loss design produces a clear reconstruction gap that enables effective anomaly detection. Extensive experiments on two widely used benchmarks and a challenging self-collected driving dataset demonstrate that our method achieves robust and superior performance over baselines.
Abstract (translated)
本文针对单场景全无监督视频异常检测这一挑战性问题，即直接使用包含正常与异常事件的原始视频进行训练和测试而无需任何标签。这与以往需要大量标注（全监督或弱监督）或仅依赖正常视频（单类分类）的工作形成鲜明对比，后者易受分布偏移和污染影响。我们提出一种基于熵引导的自编码器，通过重建误差检测异常——使正常帧重建良好而异常帧重建较差。核心思想是在自编码器中结合标准重建损失与新颖的最小潜在熵（MLE）损失。仅靠重建损失虽能因正常与异常输入的固有差异将其映射到不同的潜在簇，但也可能过度优化异常重建而难以检测。因此，MLE损失通过最小化潜在嵌入的熵来解决此问题，促使嵌入向高密度区域集中。由于正常帧在原始视频中占主导，稀疏的异常嵌入会被拉入正常簇，从而使解码器聚焦正常模式并对异常产生较差重建。这种双损失设计形成了清晰的重建差距，可实现有效异常检测。在两个广泛使用的基准数据集及具有挑战性的自采驾驶数据集上的大量实验表明，我们的方法相比基线具有稳健且卓越的性能。
URL
https://arxiv.org/abs/2603.23868
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FCL-COD: Weakly Supervised Camouflaged Object Detection with Frequency-aware and Contrastive Learning
2026-03-24 09:05:41Jingchen Ni, Quan Zhang, Dan Jiang, Keyu Lv, Ke Zhang, Chun YuanAbstract
Existing camouflage object detection (COD) methods typically rely on fully-supervised learning guided by mask annotations. However, obtaining mask annotations is time-consuming and labor-intensive. Compared to fully-supervised methods, existing weakly-supervised COD methods exhibit significantly poorer performance. Even for the Segment Anything Model (SAM), there are still challenges in handling weakly-supervised camouflage object detection (WSCOD), such as: a. non-camouflage target responses, b. local responses, c. extreme responses, and d. lack of refined boundary awareness, which leads to unsatisfactory results in camouflage scenes. To alleviate these issues, we propose a frequency-aware and contrastive learning-based WSCOD framework in this paper, named FCL-COD. To mitigate the problem of non-camouflaged object responses, we propose the Frequency-aware Low-rank Adaptation (FoRA) method, which incorporates frequency-aware camouflage scene knowledge into SAM. To overcome the challenges of local and extreme responses, we introduce a gradient-aware contrastive learning approach that effectively delineates precise foreground-background boundaries. Additionally, to address the lack of refined boundary perception, we present a multi-scale frequency-aware representation learning strategy that facilitates the modeling of more refined boundaries. We validate the effectiveness of our approach through extensive empirical experiments on three widely recognized COD benchmarks. The results confirm that our method surpasses both state-of-the-art weakly supervised and even fully supervised techniques.
Abstract (translated)
现有的伪装目标检测（COD）方法通常依赖于由掩码标注指导的全监督学习。然而，获取掩码标注耗时且劳动密集。与全监督方法相比，现有的弱监督COD方法性能显著较差。即便是Segment Anything Model（SAM），在处理弱监督伪装目标检测（WSCOD）时仍面临挑战，例如：a. 非伪装目标响应、b. 局部响应、c. 极端响应以及d. 缺乏精细化边界感知，导致在伪装场景中结果不理想。为缓解这些问题，本文提出一种基于频域感知与对比学习的WSCOD框架，命名为FCL-COD。为减轻非伪装对象响应问题，我们提出了频域感知低秩适应（FoRA）方法，将频域感知的伪装场景知识融入SAM。为克服局部与极端响应的挑战，我们引入了梯度感知对比学习方法，能有效勾勒精确的前景-背景边界。此外，为应对精细化边界感知缺失的问题，我们提出了多尺度频域表征学习策略，促进更精细化边界建模。我们在三个公认的COD基准数据集上通过大量实证实验验证了方法的有效性，结果证实我们的方法超越了最先进的弱监督乃至全监督技术。
URL
https://arxiv.org/abs/2603.22969
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Parameter-efficient Prompt Tuning and Hierarchical Textual Guidance for Few-shot Whole Slide Image Classification
2026-03-23 02:50:52Jayanie Bogahawatte, Sachith Seneviratne, Saman HalgamugeAbstract
Whole Slide Images (WSIs) are giga-pixel in scale and are typically partitioned into small instances in WSI classification pipelines for computational feasibility. However, obtaining extensive instance level annotations is costly, making few-shot weakly supervised WSI classification (FSWC) crucial for learning from limited slide-level labels. Recently, pre-trained vision-language models (VLMs) have been adopted in FSWC, yet they exhibit several limitations. Existing prompt tuning methods in FSWC substantially increase both the number of trainable parameters and inference overhead. Moreover, current methods discard instances with low alignment to text embeddings from VLMs, potentially leading to information loss. To address these challenges, we propose two key contributions. First, we introduce a new parameter efficient prompt tuning method by scaling and shifting features in text encoder, which significantly reduces the computational cost. Second, to leverage not only the pre-trained knowledge of VLMs, but also the inherent hierarchical structure of WSIs, we introduce a WSI representation learning approach with a soft hierarchical textual guidance strategy without utilizing hard instance filtering. Comprehensive evaluations on pathology datasets covering breast, lung, and ovarian cancer types demonstrate consistent improvements up-to 10.9%, 7.8%, and 13.8% respectively, over the state-of-the-art methods in FSWC. Our method reduces the number of trainable parameters by 18.1% on both breast and lung cancer datasets, and 5.8% on the ovarian cancer dataset, while also excelling at weakly-supervised tumor localization. Code at this https URL.
Abstract (translated)
全切片图像（WSIs）具有吉像素级规模，通常需在WSI分类流程中被分割为小实例以保障计算可行性。然而，获取大量实例级标注成本高昂，使得基于有限切片级标签学习的少样本弱监督WSI分类（FSWC）至关重要。近期，预训练视觉语言模型（VLMs）被应用于FSWC，但仍存在若干局限：现有FSWC中的提示调优方法显著增加了可训练参数量和推理开销；同时，当前方法会丢弃与VLM文本嵌入对齐度低的实例，可能导致信息损失。为应对这些挑战，我们提出两项关键贡献：第一，通过缩放和偏移文本编码器中的特征，提出一种新的参数高效提示调优方法，大幅降低计算成本；第二，为同时利用VLMs的预训练知识与WSIs的内在层次结构，我们引入一种不依赖硬实例过滤的软层次文本引导WSI表征学习方法。在涵盖乳腺癌、肺癌和卵巢癌类型的病理数据集上的综合评估表明，该方法在FSWC任务中相较于最新方法分别实现高达10.9%、7.8%和13.8%的持续提升。我们的方法在乳腺癌和肺癌数据集上减少18.1%的可训练参数，在卵巢癌数据集上减少5.8%，同时在弱监督肿瘤定位任务中表现卓越。代码见此链接。
URL
https://arxiv.org/abs/2603.21504
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Harmful Visual Content Manipulation Matters in Misinformation Detection Under Multimedia Scenarios
2026-03-22 04:37:04Bing Wang, Ximing Li, Changchun Li, Jinjin Chi, Tianze Li, Renchu Guan, Shengsheng WangAbstract
Nowadays, the widespread dissemination of misinformation across numerous social media platforms has led to severe negative effects on society. To address this challenge, the automatic detection of misinformation, particularly under multimedia scenarios, has gained significant attention from both academic and industrial communities, leading to the emergence of a research task known as Multimodal Misinformation Detection (MMD). Typically, current MMD approaches focus on capturing the semantic relationships and inconsistency between various modalities but often overlook certain critical indicators within multimodal content. Recent research has shown that manipulated features within visual content in social media articles serve as valuable clues for MMD. Meanwhile, we argue that the potential intentions behind the manipulation, e.g., harmful and harmless, also matter in MMD. Therefore, in this study, we aim to identify such multimodal misinformation by capturing two types of features: manipulation features, which represent if visual content has been manipulated, and intention features, which assess the nature of these manipulations, distinguishing between harmful and harmless intentions. Unfortunately, the manipulation and intention labels that supervise these features to be discriminative are unknown. To address this, we introduce two weakly supervised indicators as substitutes by incorporating supplementary datasets focused on image manipulation detection and framing two different classification tasks as positive and unlabeled learning issues. With this framework, we introduce an innovative MMD approach, titled Harmful Visual Content Manipulation Matters in MMD (HAVC-M4 D). Comprehensive experiments conducted on four prevalent MMD datasets indicate that HAVC-M4 D significantly and consistently enhances the performance of existing MMD methods.
Abstract (translated)
如今，虚假信息在众多社交媒体平台上的广泛传播已对社会造成严重负面影响。为应对这一挑战，尤其在多媒体场景下对虚假信息的自动检测已备受学术界与工业界关注，并催生了名为多模态虚假信息检测（Multimodal Misinformation Detection, MMD）的研究任务。通常，当前MMD方法侧重于捕捉多模态间的语义关联与不一致性，却常忽略多模态内容中的某些关键指标。近期研究表明，社交媒体文章中视觉内容的篡改特征可作为MMD的重要线索。同时，我们认为篡改行为背后的潜在意图（如有害与无害）同样对MMD至关重要。因此，本研究旨在通过捕捉两类特征识别此类多模态虚假信息：一是表征视觉内容是否被篡改的篡改特征，二是评估篡改性质、区分有害与无害意图的意图特征。然而，监督这些特征具有判别性的篡改标签与意图标签均未知。为解决此问题，我们引入两种弱监督指标作为替代：通过整合专注于图像篡改检测的辅助数据集，并将两种不同分类任务构建为正与未标注学习问题。基于此框架，我们提出一种创新性MMD方法，命名为《MMD中有害视觉内容篡改至关重要》（Harmful Visual Content Manipulation Matters in MMD, HAVC-M4 D）。在四个主流MMD数据集上开展的全面实验表明，HAVC-M4 D能显著且稳定地提升现有MMD方法的性能。
URL
https://arxiv.org/abs/2603.21054
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Weakly supervised multimodal segmentation of acoustic borehole images with depth-aware cross-attention
2026-03-21 09:28:05Jose Luis Lima de Jesus SilvaAbstract
Acoustic borehole images provide high-resolution borehole-wall structure, but large-scale interpretation remains difficult because dense expert annotations are rarely available and subsurface information is intrinsically multimodal. The challenge is developing weakly supervised methods combining two-dimensional image texture with depth-aligned one-dimensional well-logs. Here, we introduce a weakly supervised multimodal segmentation framework that refines threshold-guided pseudo-labels through learned models. This preserves the annotation-free character of classical thresholding and clustering workflows while extending them with denoising, confidence-aware pseudo-supervision, and physically structured fusion. We establish that threshold-guided learned refinement provides the most robust improvement over raw thresholding, denoised thresholding, and latent clustering baselines. Multimodal performance depends strongly on fusion strategy: direct concatenation provides limited gains, whereas depth-aware cross-attention, gated fusion, and confidence-aware modulation substantially improve agreement with the weak supervisory reference. The strongest model, confidence-gated depth-aware cross-attention (CG-DCA), consistently outperforms threshold-based, image-only, and earlier multimodal baselines. Targeted ablations show its advantage depends specifically on confidence-aware fusion and structured local depth interaction rather than model complexity alone. Cross-well analyses confirm this performance is broadly stable. These results establish a practical, scalable framework for annotation-free segmentation, showing multimodal improvement is maximized when auxiliary logs are incorporated selectively and depth-aware.
Abstract (translated)
声波井孔图像可提供高分辨率的井壁结构信息，但大规模解释仍面临挑战，原因在于专家密集标注极为罕见且地下信息本质上是多模态的。本研究旨在开发一种弱监督方法，将二维图像纹理与深度对齐的一维测井曲线相结合。我们提出了一种弱监督多模态分割框架，通过模型学习来细化阈值引导的伪标签。该方法既保留了经典阈值法与聚类工作流无需标注的特性，又扩展了去噪、置信度感知伪监督及物理结构化融合功能。研究证实，基于阈值的模型学习细化相比原始阈值法、去噪阈值法及潜在聚类基线均能实现最稳健的性能提升。多模态性能高度依赖融合策略：直接拼接仅带来有限提升，而深度感知交叉注意力、门控融合及置信度感知调制则显著增强了与弱监督参考标准的一致性。最优模型置信度门控深度感知交叉注意力（CG-DCA）持续优于基于阈值、仅图像及早期多模态基线。针对性消融实验表明，其优势具体源于置信度感知融合与结构化局部深度交互，而非单纯模型复杂度。跨井分析进一步验证该性能具有广泛稳定性。这些成果确立了一种实用、可扩展的无标注分割框架，证明当辅助测井曲线被选择性地深度感知整合时，多模态改进效果达到最大化。
URL
https://arxiv.org/abs/2603.20729
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Decoupled Sensitivity-Consistency Learning for Weakly Supervised Video Anomaly Detection
2026-03-20 09:15:37Hantao Zheng, Ning Han, Yawen Zeng, Hao ChenAbstract
Recent weakly supervised video anomaly detection methods have achieved significant advances by employing unified frameworks for joint optimization. However, this paradigm is limited by a fundamental sensitivity-stability trade-off, as the conflicting objectives for detecting transient and sustained anomalies lead to either fragmented predictions or over-smoothed responses. To address this limitation, we propose DeSC, a novel Decoupled Sensitivity-Consistency framework that trains two specialized streams using distinct optimization strategies. The temporal sensitivity stream adopts an aggressive optimization strategy to capture high-frequency abrupt changes, whereas the semantic consistency stream applies robust constraints to maintain long-term coherence and reduce noise. Their complementary strengths are fused through a collaborative inference mechanism that reduces individual biases and produces balanced predictions. Extensive experiments demonstrate that DeSC establishes new state-of-the-art performance by achieving 89.37% AUC on UCF-Crime (+1.29%) and 87.18% AP on XD-Violence (+2.22%). Code is available at this https URL.
Abstract (translated)
近期弱监督视频异常检测方法通过采用统一框架进行联合优化取得了显著进展。然而，该范式受限于根本性的敏感性-稳定性权衡问题：检测瞬态异常与持续异常的目标相互冲突，导致预测结果碎片化或响应过度平滑。为克服这一局限，我们提出DeSC——一种新颖的解耦敏感性-一致性框架，该框架采用不同的优化策略训练两个专用流。时序敏感性流采用激进优化策略以捕捉高频突变，而语义一致性流则施加稳健约束以维持长期连贯性并降低噪声。两个流通过协同推理机制融合其互补优势，该机制能减少个体偏差并产生均衡预测。大量实验表明，DeSC在UCF-Crime上达到89.37%的AUC（+1.29%），在XD-Violence上达到87.18%的AP（+2.22%），建立了新的最先进性能。代码已公开于此https链接。
URL
https://arxiv.org/abs/2603.19780
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WeNLEX: Weakly Supervised Natural Language Explanations for Multilabel Chest X-ray Classification
2026-03-19 10:57:44Isabel Rio-Torto, Jaime S. Cardoso, Lu\'is F. TeixeiraAbstract
Natural language explanations provide an inherently human-understandable way to explain black-box models, closely reflecting how radiologists convey their diagnoses in textual reports. Most works explicitly supervise the explanation generation process using datasets annotated with explanations. Thus, though plausible, the generated explanations are not faithful to the model's reasoning. In this work, we propose WeNLEX, a weakly supervised model for the generation of natural language explanations for multilabel chest X-ray classification. Faithfulness is ensured by matching images generated from their corresponding natural language explanations with original images, in the black-box model's feature space. Plausibility is maintained via distribution alignment with a small database of clinician-annotated explanations. We empirically demonstrate, through extensive validation on multiple metrics to assess faithfulness, simulatability, diversity, and plausibility, that WeNLEX is able to produce faithful and plausible explanations, using as little as 5 ground-truth explanations per diagnosis. Furthermore, WeNLEX can operate in both post-hoc and in-model settings. In the latter, i.e., when the multilabel classifier is trained together with the rest of the network, WeNLEX improves the classification AUC of the standalone classifier by 2.21%, thus showing that adding interpretability to the training process can actually increase the downstream task performance. Additionally, simply by changing the database, WeNLEX explanations are adaptable to any target audience, and we showcase this flexibility by training a layman version of WeNLEX, where explanations are simplified for non-medical users.
Abstract (translated)
URL
https://arxiv.org/abs/2603.18752
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VirPro: Visual-referred Probabilistic Prompt Learning for Weakly-Supervised Monocular 3D Detection
2026-03-18 08:23:55Chupeng Liu, Jiyong Rao, Shangquan Sun, Runkai Zhao, Weidong CaiAbstract
Monocular 3D object detection typically relies on pseudo-labeling techniques to reduce dependency on real-world annotations. Recent advances demonstrate that deterministic linguistic cues can serve as effective auxiliary weak supervision signals, providing complementary semantic context. However, hand-crafted textual descriptions struggle to capture the inherent visual diversity of individuals across scenes, limiting the model's ability to learn scene-aware representations. To address this challenge, we propose Visual-referred Probabilistic Prompt Learning (VirPro), an adaptive multi-modal pretraining paradigm that can be seamlessly integrated into diverse weakly supervised monocular 3D detection frameworks. Specifically, we generate a diverse set of learnable, instance-conditioned prompts across scenes and store them in an Adaptive Prompt Bank (APB). Subsequently, we introduce Multi-Gaussian Prompt Modeling (MGPM), which incorporates scene-based visual features into the corresponding textual embeddings, allowing the text prompts to express visual uncertainties. Then, from the fused vision-language embeddings, we decode a prompt-targeted Gaussian, from which we derive a unified object-level prompt embedding for each instance. RoI-level contrastive matching is employed to enforce modality alignment, bringing embeddings of co-occurring objects within the same scene closer in the latent space, thus enhancing semantic coherence. Extensive experiments on the KITTI benchmark demonstrate that integrating our pretraining paradigm consistently yields substantial performance gains, achieving up to a 4.8% average precision improvement than the baseline.
Abstract (translated)
URL
https://arxiv.org/abs/2603.17470
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Something from Nothing: Data Augmentation for Robust Severity Level Estimation of Dysarthric Speech
2026-03-16 23:00:07Jaesung Bae, Xiuwen Zheng, Minje Kim, Chang D. Yoo, Mark Hasegawa-JohnsonAbstract
Dysarthric speech quality assessment (DSQA) is critical for clinical diagnostics and inclusive speech technologies. However, subjective evaluation is costly and difficult to scale, and the scarcity of labeled data limits robust objective modeling. To address this, we propose a three-stage framework that leverages unlabeled dysarthric speech and large-scale typical speech datasets to scale training. A teacher model first generates pseudo-labels for unlabeled samples, followed by weakly supervised pretraining using a label-aware contrastive learning strategy that exposes the model to diverse speakers and acoustic conditions. The pretrained model is then fine-tuned for the downstream DSQA task. Experiments on five unseen datasets spanning multiple etiologies and languages demonstrate the robustness of our approach. Our Whisper-based baseline significantly outperforms SOTA DSQA predictors such as SpICE, and the full framework achieves an average SRCC of 0.761 across unseen test datasets.
Abstract (translated)
URL
https://arxiv.org/abs/2603.15988
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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.
Abstract (translated)
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.
Abstract (translated)
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.
Abstract (translated)
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.
Abstract (translated)
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.
Abstract (translated)
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.
Abstract (translated)
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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