This work proposes a teacher-student semi-supervised learning framework that generates high-quality pseudo-labels from unlabeled data through confidence-aware map refinement, and introduces a spatial clipping technique that selectively preserves high-confidence regions while removing unreliable segments.
Abstract
A critical challenge in deploying online HD map construction systems to real-world scenarios is the scarcity of labeled training data, which limits model generalization in diverse environments. To address this limitation, we propose a teacher-student semi-supervised learning (SSL) framework that generates high-quality pseudo-labels from unlabeled data through confidence-aware map refinement. Our approach first trains a teacher model on limited labeled data, then leverages Beta-distribution-based confidence maps to assess the reliability of predicted map elements across temporal observations. Unlike conventional filtering methods that discard entire elements, we introduce a spatial clipping technique that selectively preserves high-confidence regions while removing unreliable segments. The refined map elements serve as map priors that improve the teacher model's prediction accuracy on unlabeled data in a second pass. These enhanced predictions become pseudo-labels for training a student model from scratch, followed by fine-tuning on the original labeled data. Experimental results on the nuScenes dataset demonstrate that our teacher-student framework with refined pseudo-labels improves performance by +6.1 mAP under a low-label regime compared to training on labeled data alone, offering a practical solution to the labeled data scarcity problem in online HD map construction.
In semi-supervised object detection (SSOD), due to the limited availability of labeled data, the quality and quantity of pseudo labels generated from unlabeled images are crucial for model training. Our study reveals that in the early stages of training, the number of usable pseudo labels is very low, which hampers the model's ability to converge quickly. Furthermore, throughout the training process, even though pseudo-labels are generated with high confidence, a significant number of labeling errors still occur. To address these challenges, we propose a novel framework, Generation-to-Optimization of Pseudo Labels (GO-PL), which comprehensively improves the model's performance in both the pseudo labels generation stage and the pseudo labels optimization stage. Specifically, we design a CLIP-based Pseudo Label Generation (CPLG) module, which matches proposals from the teacher model with CLIP text embeddings to generate semantically consistent high-confidence pseudo labels. These labels are further integrated into student training via an auxiliary loss, enabling early-stage guidance with stable and informative supervision signals. We also introduce Pixel-guided Instance Weighting (PIW) module that utilizes the SAM to assess the pixel-level quality of pseudo labels generated by the teacher model. Combined with a local voting mechanism, PIW dynamically assigns weights to each pseudo-label, thereby optimizing the impact of labels with varying quality on gradient updates. GO-PL demonstrates strong performance on standard SSOD benchmarks, including the MS-COCO and PASCAL VOC datasets. Notably, it achieves 26.7% mAP with only 1% labeled data on the MS-COCO, surpassing the previous best approach by 1.7%.
Xi Yang, Penghui Li, Nannan Wang· IEEE Transactions on Image P...· 0 citations
Semi-supervised learning (SSL) is an effective approach to leverage limited labeled data alongside abundant unlabeled data. While methods like MarginMatch have shown promise by using the average pseudo margin (APM) to evaluate pseudo-label reliability, they face two limitations: (1) APM struggles to distinguish correctly and incorrectly pseudo-labeled classes for hard-to-learn samples, and (2) fixed-percentile thresholding leads to suboptimal pseudo-label utilization throughout training. To address these challenges, we propose APIMatch, a novel SSL framework introducing the average pseudo integrated margin (APIM) metric and a negative-sample-aware dynamic percentile thresholding strategy. The core novelty lies in jointly modeling the pseudo-labeled class and competitive non-pseudo-labeled classes to accurately re-evaluate hard-to-learn samples, coupled with a confidence-distribution-aware adaptive threshold. Specifically, APIM jointly considers the logit differences between the pseudo-labeled class and both the largest and second-largest non-pseudo-labeled classes, enabling more accurate confidence evaluation for hard-to-learn samples. In addition, our dynamic thresholding strategy constructs a pseudo-negative-sample reference set to model confidence distributions and adaptively adjusts the percentile threshold based on training progression, improving both recall in early stages and precision in later stages. Extensive experiments on CIFAR-10, CIFAR-100, and STL-10 demonstrate that APIMatch achieves competitive performance against state-of-the-art methods, particularly in extremely low-label scenarios. Under the 1 label per class setting, APIMatch achieves error rates of
1.84
%
and
36.7
%
on CIFAR-10 and CIFAR-100, respectively. On STL-10 with only 4 labels per class, APIMatch achieves an error rate of
18.47
%
±
0.65
%
, representing a
6.9
%
absolute accuracy improvement over MarginMatch, demonstrating strong robustness under severe label scarcity.
Xianmin Wang, Dong-Yuan Tian, Jing Li· The European Journal on Arti...· 0 citations
A meta-learning-based dual-model disambiguation for partial-label learning (MD2PLL), a robust framework for candidate-label disambiguation under ambiguous annotations, is proposed and achieves competitive performance on Yahoo! News and Soccer Player.
Xiaohui Zhong, Xiaowen Yan, Jiayin Song et al.· The Visual Computer· 0 citations
This paper proposes a novel Partial label-based Self-training framework (PaSta) that leverages partial label learning technique to overcome the limitations of existing methods and designs a partial label-based classification model with two well-crafted loss functions to guide the model learning at both label and representation spaces.
Yujing Liu, Yixin Liu, Yu Zheng et al.· 0 citations
Semi-supervised semantic segmentation has long turned on one question, which pseudo-labels to trust, and a generation of selection rules, dynamic thresholds, per-class curricula, soft confidence weights, answered it for the noisy, under-confident ResNet teachers of their day. Self-supervised foundation encoders change the regime: with a DINOv2 teacher, confidence saturates, so the filtering that helped a weak teacher can hurt a strong one. We propose CW-BASS v2, a saturation-aware pseudo-label selection method that reads the teacher's confidence regime rather than committing to one rule. It pairs held-out calibration, an unbiased per-class noise estimate, with a self-adaptive confidence floor that provably bounds retention away from 1, and combines them in a one-pass gate: measure the reliability of the teacher's confident set, pi_kept = Pr[correct | c>= tau], on a held-out slice, and filter strictly when it meets the confidence demanded (pi_kept>= tau), falling back to the adaptive floor otherwise. The boundary is the pre-existing operating threshold, not a value tuned to mIoU, and across six DINOv2 teachers it makes the correct strict-vs-floor call blind. CW-BASS v2 thus recovers the UniMatch V2 operating point on the saturated benchmarks by selecting strict (Pascal VOC 1/8 87.4 against its reported 87.9; Cityscapes within 0.5), and improves on it where the confident set is unreliable (pi_kept ~ 89%, ADE20K), where the floor edges ahead (+1.5 mIoU, single seed). The gate is principled because the failure it avoids is measured, not assumed: on a reliable, saturated teacher the confidence distribution's dynamic range collapses (98% of Pascal pixels>= 0.95), so an adaptive cutoff floods the retention mask and self-training decays into confirmation bias.
This work introduces a novel framework, Gaussian Bridge Consistency (GBC), to address challenges of semi-supervised learning by constructing semantic interpolation paths between unlabeled samples and high-quality class anchors, and proposes BridgeMix, a confidence-aware feature mixing strategy that interpolates both sample and anchor pairs to amplify cross-sample generalization.
Hong-Yang He, Xin-Yuan Song, Yan Zhong et al.· 0 citations
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