Trusted multi-view classification (TMC) aims to improve prediction reliability by integrating evidence from multiple views. Existing TMC methods extract evidence from single view and use a regularization term to shape the evidence distribution. However, existing methods typically enforce a uniform regularization objective across all views, overlooking critical view-specific biases: intra-view class ambiguity caused by confusable features and inter-view quality disparities reflected in evidence uncertainty. To address these issues, we propose an adaptive regularization strategy that enhances robustness on two levels. At the intra-view level, it quantifies feature ambiguity to apply targeted relaxation to confusable classes, preventing over-penalization of inherent uncertainty. At the inter-view level, it evaluates relative view quality to impose stronger constraints on unreliable views and suppress noise from low-quality ones. Extensive experiments across multiple benchmarks demonstrate the superiority and reliability of the proposed method.

Active retrieval-augmented generation (RAG) triggers external knowledge retrieval during generation based on model-side uncertainty signals to support knowledge-intensive, multi-hop reasoning. However, existing methods often retrieve only after producing a complete answer, failing to surface and fill information gaps in time; moreover, relying on a single internal signal as the trigger cannot adequately capture the multifaceted nature of uncertainty. We therefore propose a conflict-aware active RAG framework. We first decompose complex questions into a sequence of step-level sub-problems. At each step, we quantify local distributional uncertainty via a sliding-window peak token entropy, and estimate cross-sample consensus via the variation ratio computed over multiple Monte Carlo samples. After calibrating both signals onto a probabilistic scale, we quantify their conflict using a symmetric, bounded divergence over Bernoulli parameters, and fuse the three quantities into a single uncertainty score that gates retrieval. Experiments demonstrate the effectiveness of our framework.

Cross-modal retrieval aims to match videos and texts by mapping them into a shared feature space. Most existing approaches achieve alignment through contrastive learning based on one-to-one supervised pairs. However, these methods rely too much on supervised signals and do not fully use the unsupervised semantic relationships within each modality. As a result, samples that are semantically similar may be spread in the shared space, which hurts retrieval performance. To solve this problem, we propose a method called Leveraging Intra-Modal Consistency for Cross-Modal Alignment and Retrieval (LICA). Our method introduces a consistency constraint between intra-modal similarities and cross-modal similarity distributions. In this way, samples that are close in meaning stay closer together in the shared space. Experiments on standard text-video retrieval benchmarks show that LICA helps optimize the distribution of the cross-modal feature space and improves retrieval accuracy.

Medical image diagnosis inherently involves uncertainty due to artifacts, occlusions, and ambiguous visual patterns, often leading to high inter-observer variability. While deep neural networks offer strong predictive performance, their outputs tend to be overconfident and poorly calibrated, limiting their clinical reliability. We propose Conformal Focal Loss (CFL), a principled approach that leverages the focal loss and the statistical validity of conformal prediction to better characterize diagnostic uncertainty. By emphasizing hard or ambiguous examples, CFL enables more accurate estimation of both predictive confidence and ambiguity. We evaluate CFL on diagnostic tasks using both clean and noise-augmented datasets, demonstrating its ability to effectively identify uncertain cases while maintaining robust classification performance under label noise.

Trustworthy multi-view classification is essential for safety-critical applications, yet existing methods often fail when view quality degrades at test time due to noise or domain shifts. We propose a novel test-time adaptive fusion framework that leverages Conformal Prediction (CP) to transform pre-trained single-view classifier outputs into robust evidence representations. By integrating CP with Dempster-Shafer Theory, our approach dynamically generates well-calibrated evidence for each view, enabling reliable fusion under arbitrary view conditions without retraining. Extensive experiments demonstrate that our method achieves competitive performance on clean data and significantly outperforms prior methods in the presence of view corruption, offering a practical solution for robust multi-view classification in real-world scenarios.

Gradient-based methods provide a principled framework for Active Learning (AL), effectively quantifying a sample’s informativeness through the uncertainty captured from the model’s internal dynamics. However, their effectiveness is often hindered by the failure to disentangle epistemic and aleatoric uncertainty. This can lead to the suboptimal selection of noisy outliers over truly informative samples. To address this, we propose Dual-View Gradient Probes (DVGradProb), a framework that disentangles these uncertainties by probing gradients from two complementary spaces. Specifically, epistemic uncertainty is captured from the parameter space and introduce a novel metric for aleatoric uncertainty from the feature space. These metrics are integrated into a ratio-based acquisition function designed to filter out noisy outliers while prioritizing truly informative samples, those with high epistemic but low aleatoric uncertainty. Experiments on MNIST, CIFAR-10, and SVHN demonstrate that DVGradProb robustly outperforms baselines, validating the effectiveness of this uncertainty disentanglement strategy.

Labeling cerebral vessels requires domain knowledge in neurology and could be extremely laborious, and there is a scarcity of public annotated cerebrovascular datasets. Traditional machine learning or statistical models could yield decent results on thick vessels with high contrast while having poor performance on those regions of low contrast. In our work, we employ a statistic model as noisy labels and propose a Transformer-based architecture which utilizes Hessian shape prior as soft supervision. It enhances the learning ability of the network to tubular structures, so that the model can make more accurate predictions on refined cerebrovascular segmentation. Furthermore, to combat the overfitting towards noisy labels as model training, we introduce an effective label extension strategy that only calls for a few manual strokes on one sample. These supplementary labels are not used for supervision but only as an indicator to tell where the model keeps the most generalization capability, so as to further guide the model selection in validation. Our experiments are carried out on a public TOF-MRA dataset from MIDAS data platform, and the results demonstrate that our method shows superior performance on cerebrovascular segmentation which achieves Dice of 0.831±0.040 in the dataset.

Bladder cancer is a malignant disease with substantial morbidity and mortality. Bladder cancer staging is crucial to determine the effective treatments of bladder tumors in clinic. As to the superiority of feature learning, Deep Convolutional Neural Networks (DCNN) are widely used to predict the cancer stage based on medical images. However, most existing DCNN-based cancer staging methods are data-driven and neglect the domain knowledge and experiences of clinicians. Besides, the deep neural networks are short of model interpretability and may lead to risky diagnosis. To tackle the problems, we construct the diagnosis rules of bladder cancer staging based on the clinical experiences of tumor penetration into bladder wall. The diagnosis rules are extracted from Magnetic Resonance (MR) images and further integrated into DCNN for joint identification of tumor stage. The experiments validate that the integrated rules improve the model interpretability and guide DCNN to focus on the regions of tumor penetration and thereby produce precise prediction of cancer staging.