Uncertainty-aware consistency learning is one of the reliable approaches in semi-supervised medical image segmentation, enforcing robust model predictions under various perturbations. However, existing methods often rely on multiple stochastic predictions or dual-network/decoder discrepancies to estimate uncertainty, which increases computational cost and discards uncertain regions, potentially missing complex structures such as ambiguous lesion boundaries. To address these challenges, we introduce a Dual Uncertainty-Guided Consistency and Regional Contrastive Learning (DUCore) framework. DUCore improves segmentation robustness by integrating two complementary loss functions within consistency learning. The dual uncertainty-guided consistency loss (DuCL) adaptively calibrates the prediction alignment by prioritizing uncertain regions. DuCL uses deterministic single-pass uncertainty estimation, employing entropy-based calibration for aleatoric uncertainty and Proxy Dirichlet calibration for epistemic uncertainty. These uncertainty measures are computed directly from network output, and moderately uncertain regions are weighted instead of being discarded, which preserves valuable learning signals. The Regional Contrastive Loss (ReCL) further refines feature separability using boundary- and gradient-based hard negative mining in the encoded representation space. By explicitly targeting structural ambiguities, ReCL distinguishes lesion and organ edges from visually similar boundary-adjacent regions and mitigates intensity overlaps in gradient-rich transitions. As a result, DUCore is able to delineate fine structures and complex boundaries with higher precision. Extensive experiments on various medical segmentation benchmarks reveal that DUCore outperforms existing consistency methods.

Semi-supervised learning in medical image segmentation leverages unlabeled data to reduce annotation burdens through consistency learning. However, current methods struggle with class imbalance and high uncertainty from pathology variations, leading to inaccurate segmentation in 3D medical images. To address these challenges, we present DyCON, a Dynamic Uncertainty-aware Consistency and Contrastive Learning framework that enhances the generalization of consistency methods with two complementary losses: Uncertainty-aware Consistency Loss (UnCL) and Focal Entropy-aware Contrastive Loss (FeCL). UnCL enforces global consistency by dynamically weighting the contribution of each voxel to the consistency loss based on its uncertainty, preserving high-uncertainty regions instead of filtering them out. Initially, UnCL prioritizes learning from uncertain voxels with lower penalties, encouraging the model to explore challenging regions. As training progress, the penalty shift towards confident voxels to refine predictions and ensure global consistency. Meanwhile, FeCL enhances local feature discrimination in imbalanced regions by introducing dual focal mechanisms and adaptive confidence adjustments into the contrastive principle. These mechanisms jointly prioritizes hard positives and negatives while focusing on uncertain sample pairs, effectively capturing subtle lesion variations under class imbalance. Extensive evaluations on four diverse medical image segmentation datasets (ISLES’22, BraTS’19, LA, Pancreas) show DyCON’s superior performance against SOTA methods1.

This paper presents a summary of the 2025 Sclera Segmentation Benchmarking Competition (SSBC), which focused on the development of privacy-preserving sclera-segmentation models trained using synthetically generated ocular images. The goal of the competition was to evaluate how well models trained on synthetic data perform in comparison to those trained on real-world datasets. The competition featured two tracks: (i) one relying solely on synthetic data for model development, and (ii) one combining/mixing synthetic with (a limited amount of) real-world data. A total of nine research groups submitted diverse segmentation models, employing a variety of architectural designs, including transformer-based solutions, lightweight models, and segmentation networks guided by generative frameworks. Experiments were conducted across three evaluation datasets containing both synthetic and real-world images, collected under diverse conditions. Results show that models trained entirely on synthetic data can achieve competitive performance, particularly when dedicated training strategies are employed, as evidenced by the top performing models that achieved F1scores of over 0.8 in the synthetic data track. Moreover, performance gains in the mixed track were often driven more by methodological choices rather than by the inclusion of real data, highlighting the promise of synthetic data for privacy-aware biometric development. The code and data for the competition is available at: https://github.com/dariant/SSBC_2025.

Intelligent techniques, including artificial intelligence and deep learning, normally perform on complete data without missing data. Multiple imputation is indispensable for addressing missing data resulting in unbiased estimates and dealing with uncertainty by providing more valid results. Most state-of-the-art techniques focus on high-missing rates (around 50%–60%) and short missing gaps, while imputation for extreme missing gaps and missing rates is an important challenge for multivariate time-series data generated through the Internet of Things (IoT). Hence, we propose an lightweight-window-portion-based multiple imputation (LWPMI) based on multivariate variables, correlation, data fusion, regression, and multiple imputations. We conduct extensive experiments by generating extreme missing gaps and high-missing rates ranging from 10% to 90% on data generated by sensors. We also investigate different sets of feature to examine how LWPMI works when features have high, weak, or a mixture of high and weak correlation. All the obtained results prove LWPMI outperforms baseline techniques in preserving pattern, structure, and trend in both 90% extreme missing gap and missing rates.

Semi-supervised video learning is an increasingly popular approach for improving video understanding tasks by utilizing large-scale unlabeled videos along with a few labels. Recent studies have shown that multimodal contrastive learning and consistency regularization are effective techniques for generating high-quality pseudo-labels for semi-supervised action recognition. However, existing pseudo-labeling approaches are solely based on the model's class predictions and can suffer from confirmation biases due to the accumulation of false predictions. To address this issue, we propose exploiting audio-visual feature correlations to achieve high-quality pseudo-labels instead of relying on model confidence. To achieve this goal, we introduce Audio-visual Contrastive and Consistency Learning (AvCLR) for semi-supervised action recognition. AvCLR generates reliable pseudo-labels from audio-visual feature correlations using deep embedded clustering to mitigate confirmation biases. Additionally, AvCLR introduces two contrastive modules: intra-modal contrastive learning (ImCL) and cross-modal contrastive learning (XmCL) to discover complementary information from audio-visual alignments. The ImCL module learns informative representations within audio and video independently, while the XmCL module aims to leverage global high-level features of audio-visual information. Furthermore, the XmCL is constrained by introducing intra-instance negatives from one modality to the other. We jointly optimize the model with ImCL, XmCL, and consistency regularization in an end-to-end semi-supervised manner. Experimental results have demonstrated that the proposed AvCLR framework is effective in reducing confirmation biases and outperforms existing confidence-based semi-supervised action recognition methods.

Self-supervised contrastive learning has shown a significant improvement in performance for action recognition tasks by discovering useful signals from unlabeled videos. Nevertheless, the unique features of existing video benchmark datasets have led the learned video representations to be contextually biased toward dominant backgrounds and scene correlations. Thus, ultimately leading to poor generalizations on scene-invariant action recognition. Therefore, we propose Actor-aware Self-supervised Learning for Semi-supervised Video Representation Learning (ActorSL). We aligned localized actors and their corresponding scene information to encourage the model to learn discriminative regions and mitigate the model’s dependency on the video background during contrastive training. Furthermore, we present an inter-video Background Mixing (iBM) augmentation strategy to introduce scene consistency into the model. We patch inter-video crops of four randomly selected frames for iBM to create a unique frame for each video. The patched frame is blended with the target video frames to generate a spatially augmented sample. Then, the actor-scene aligned features and features of iBM-augmented videos are utilized to optimize contrastive loss and consistency regularization jointly in a semi-supervised way. Moreover, iBM combines the one-hot-encoded labels of patches with the label of the target video as a label smoothing regularizer to soften the decision boundaries of the semi-supervised model. Our experimental results reveal that, ActorSL notably improved current state-of-the-art semi-supervised methods on the Kinetics-400, UCF101, and HMDB51 datasets under a low-label regime. Code released athttps://github.com/Endarzboy/ActorSL.

Self-supervised learning has considerably improved video representation learning by discovering supervisory signals automatically from unlabeled videos. However, due to the scene-biased nature of existing video datasets, the current methods are biased to the dominant scene context during action inference. Hence, this paper proposes Background Patching (BP), a scene-debiasing augmentation strategy to alleviate the model reliance on the video background in a self-supervised contrastive manner. The BP reduces the negative influence of the video background by mixing a randomly patched frame to the video background. BP randomly crops four frames from four different videos and patches them to construct a new frame for each video separately. The patched frame is mixed with all frames of the target video to produce a spatially distorted video sample. Then, we use existing self-supervised contrastive frameworks to pull representations of the distorted and original videos closer together. Moreover, BP mixes the semantic labels of patches with the target video's label, resulting in the regularization of the contrastive model to soften the decision boundaries in the embedding space. Therefore, the model is explicitly constrained to suppress the background influence by emphasizing more on the motion changes. The extensive experimental results show that our BP significantly improved the performance of various video understanding downstream tasks including action recognition, action detection, and video retrieval.

The unique features of existing video datasets have led self-supervised contrastive learning to scene correlations and background biases, resulting in poor generalization in scene-invariant action recognition. Therefore, we propose Actor-aware Contrastive Learning for semi-supervised action recognition (ActorCLR). We employ localized actors to encourage the model to learn discriminative regions and mitigate the model's reliance on the video background during contrastive training. Furthermore, we introduce Inter-video Background Mixing (iBM) augmentation strategy to inject scene-invariance into the model. For iBM, we patch inter-video crops of four randomly selected frames to create a distinct frame for each video individually. The patched frame is mixed with the target video frames to produce a spatially distorted sample. Then, we jointly optimize contrastive loss and consistency regularization with localized actors and corresponding iBM-augmented videos in a semi-supervised manner. iBM also mixes the one-hot-encoded labels of patches with the target video's label, which softens the decision boundaries of the semi-supervised model. Our experimental results show that ActorCLR significantly improved action recognition on Kinetics-400, UCF101, and HMDB51 datasets under a low-label regime.