Prototype-Guided Pseudo-Labeling with Neighborhood-Aware Consistency for Unsupervised Adaptation

• URL: http://arxiv.org/abs/2507.22075v1
• Date: Tue, 22 Jul 2025 19:08:24 GMT
• Title: Prototype-Guided Pseudo-Labeling with Neighborhood-Aware Consistency for Unsupervised Adaptation
• Authors: Eman Ali, Chetan Arora, Muhammad Haris Khan,
• Abstract summary: In unsupervised adaptation for vision-language models such as CLIP, pseudo-labels from zero-shot predictions often exhibit significant noise.<n>We propose a novel adaptive pseudo-labeling framework that enhances CLIP's adaptation performance by integrating prototype consistency and neighborhood-based consistency.<n>Our method achieves state-of-the-art performance in unsupervised adaptation scenarios, delivering more accurate pseudo-labels while maintaining computational efficiency.
• Score: 12.829638461740759
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In unsupervised adaptation for vision-language models such as CLIP, pseudo-labels derived from zero-shot predictions often exhibit significant noise, particularly under domain shifts or in visually complex scenarios. Conventional pseudo-label filtering approaches, which rely on fixed confidence thresholds, tend to be unreliable in fully unsupervised settings. In this work, we propose a novel adaptive pseudo-labeling framework that enhances CLIP's adaptation performance by integrating prototype consistency and neighborhood-based consistency. The proposed method comprises two key components: PICS, which assesses pseudo-label accuracy based on in-class feature compactness and cross-class feature separation; and NALR, which exploits semantic similarities among neighboring samples to refine pseudo-labels dynamically. Additionally, we introduce an adaptive weighting mechanism that adjusts the influence of pseudo-labeled samples during training according to their estimated correctness. Extensive experiments on 11 benchmark datasets demonstrate that our method achieves state-of-the-art performance in unsupervised adaptation scenarios, delivering more accurate pseudo-labels while maintaining computational efficiency.

Related papers

• Towards Fine-Grained Adaptation of CLIP via a Self-Trained Alignment Score [11.74414842618874]
  We show that modeling fine-grained cross-modal interactions during adaptation produces more accurate, class-discriminative pseudo-labels.<n>We introduce Fine-grained Alignment and Interaction Refinement (FAIR), an innovative approach that dynamically aligns localized image features with descriptive language embeddings.<n>Our approach, FAIR, delivers a substantial performance boost in fine-grained unsupervised adaptation, achieving a notable overall gain of 2.78%.
  arXiv  Detail & Related papers  (2025-07-13T12:38:38Z)
• Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation [5.7977777220041204]
  A common practice in pseudo-supervision is filtering pseudo-labels based on pre-defined confidence thresholds or entropy.<n>We propose Ensemble-of-Confidence Reinforcement (ENCORE), a dynamic feedback-driven thresholding strategy for pseudo-label selection.<n>Our method seamlessly integrates into existing pseudo-supervision frameworks and significantly improves segmentation performance.
  arXiv  Detail & Related papers  (2025-05-12T15:58:08Z)
• Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning [81.83013974171364]
  Semi-supervised multi-label learning (SSMLL) is a powerful framework for leveraging unlabeled data to reduce the expensive cost of collecting precise multi-label annotations.<n>Unlike semi-supervised learning, one cannot select the most probable label as the pseudo-label in SSMLL due to multiple semantics contained in an instance.<n>We propose a dual-perspective method to generate high-quality pseudo-labels.
  arXiv  Detail & Related papers  (2024-07-26T09:33:53Z)
• Rethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning [10.396596055773012]
  We propose a novel complementary learning approach to enhance test-time adaptation. In test-time adaptation tasks, information from the source domain is typically unavailable. We highlight that the risk function of complementary labels agrees with their Vanilla loss formula.
  arXiv  Detail & Related papers  (2023-01-15T03:36:33Z)
• Neighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification [80.98291772215154]
  Unsupervised person re-identification (ReID) aims at learning discriminative identity features for person retrieval without any annotations. Recent advances accomplish this task by leveraging clustering-based pseudo labels. We propose a Neighbour Consistency guided Pseudo Label Refinement framework.
  arXiv  Detail & Related papers  (2022-11-30T09:39:57Z)
• Contrastive Test-Time Adaptation [83.73506803142693]
  We propose a novel way to leverage self-supervised contrastive learning to facilitate target feature learning. We produce pseudo labels online and refine them via soft voting among their nearest neighbors in the target feature space. Our method, AdaContrast, achieves state-of-the-art performance on major benchmarks.
  arXiv  Detail & Related papers  (2022-04-21T19:17:22Z)
• Rethinking Pseudo Labels for Semi-Supervised Object Detection [84.697097472401]
  We introduce certainty-aware pseudo labels tailored for object detection. We dynamically adjust the thresholds used to generate pseudo labels and reweight loss functions for each category to alleviate the class imbalance problem. Our approach improves supervised baselines by up to 10% AP using only 1-10% labeled data from COCO.
  arXiv  Detail & Related papers  (2021-06-01T01:32:03Z)
• Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification [137.9939571408506]
  We estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels. Our uncertainty-guided optimization brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-16T04:09:04Z)
• Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation [116.48885692054724]
  We propose a reinforcement learning based selective pseudo-labeling method for semi-supervised domain adaptation. We develop a deep Q-learning model to select both accurate and representative pseudo-labeled instances. Our proposed method is evaluated on several benchmark datasets for SSDA, and demonstrates superior performance to all the comparison methods.
  arXiv  Detail & Related papers  (2020-12-07T03:37:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.