From Model Uncertainty to Human Attention: Localization-Aware Visual Cues for Scalable Annotation Review

• URL: http://arxiv.org/abs/2605.12303v1
• Date: Tue, 12 May 2026 15:54:05 GMT
• Title: From Model Uncertainty to Human Attention: Localization-Aware Visual Cues for Scalable Annotation Review
• Abstract summary: We study the effect of visualizing spatial uncertainty via a purpose-built interface.<n>In a controlled study with 120 participants, those receiving uncertainty cues achieve higher label quality while being faster overall.<n>These findings establish localization uncertainty as a lever to improve human-in-the-loop annotation.
• Score: 6.286472799566988
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-quality labeled data is essential for training robust machine learning models, yet obtaining annotations at scale remains expensive. AI-assisted annotation has therefore become standard in large-scale labeling workflows. However, in tasks where model predictions carry two independent components, a class label and spatial boundaries, a model may classify an object with high confidence while mislocalizing it. Existing AI-assisted workflows offer annotators no signal about where spatial errors are most likely. Without such guidance, humans may systematically underinspect subtly misplaced boxes. We address this by studying the effect of visualizing spatial uncertainty via a purpose-built interface. In a controlled study with 120 participants, those receiving uncertainty cues achieve higher label quality while being faster overall. A box-level analysis confirms that the cues redirect annotator effort toward high-uncertainty predictions and away from well-localized boxes. These findings establish localization uncertainty as a lever to improve human-in-the-loop annotation. Code is available at https://mos-ks.github.io/MUHA/.

Related papers

• Learning Uncertainty from Sequential Internal Dispersion in Large Language Models [52.29267172760918]
  Uncertainty estimation is a promising approach to detect hallucinations in large language models.<n>Recent approaches commonly depend on model internal states to estimate uncertainty.<n>We present Sequential Internal Variance Representation (SIVR), a supervised hallucination detection framework.
  arXiv  Detail & Related papers  (2026-04-17T06:31:29Z)
• A Model of Artificial Jagged Intelligence [0.0]
  Generative AI systems often display highly uneven performance across tasks that appear nearby''<n>We call this phenomenon Artificial Jagged Intelligence (AJI)<n>This paper develops a tractable economic model of AJI that treats adoption as an information problem.
  arXiv  Detail & Related papers  (2026-01-12T14:27:30Z)
• Virtual Category Learning: A Semi-Supervised Learning Method for Dense Prediction with Extremely Limited Labels [63.16824565919966]
  This paper proposes to use confusing samples proactively without label correction. A Virtual Category (VC) is assigned to each confusing sample in such a way that it can safely contribute to the model optimisation. Our intriguing findings highlight the usage of VC learning in dense vision tasks.
  arXiv  Detail & Related papers  (2023-12-02T16:23:52Z)
• Improving a Named Entity Recognizer Trained on Noisy Data with a Few Clean Instances [55.37242480995541]
  We propose to denoise noisy NER data with guidance from a small set of clean instances. Along with the main NER model we train a discriminator model and use its outputs to recalibrate the sample weights. Results on public crowdsourcing and distant supervision datasets show that the proposed method can consistently improve performance with a small guidance set.
  arXiv  Detail & Related papers  (2023-10-25T17:23:37Z)
• XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners [71.8257151788923]
  We propose a novel Explainable Active Learning framework (XAL) for low-resource text classification.<n>XAL encourages classifiers to justify their inferences and delve into unlabeled data for which they cannot provide reasonable explanations.<n>Experiments on six datasets show that XAL achieves consistent improvement over 9 strong baselines.
  arXiv  Detail & Related papers  (2023-10-09T08:07:04Z)
• From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space [13.763716495058294]
  Deep Neural Networks are prone to learning spurious correlations embedded in the training data, leading to potentially biased predictions. This poses risks when deploying these models for high-stake decision-making, such as in medical applications. We present a novel method for model correction on the concept level that explicitly reduces model sensitivity towards biases via gradient penalization.
  arXiv  Detail & Related papers  (2023-08-18T10:07:46Z)
• Semi-supervised Object Detection via Virtual Category Learning [68.26956850996976]
  This paper proposes to use confusing samples proactively without label correction. Specifically, a virtual category (VC) is assigned to each confusing sample. It is attributed to specifying the embedding distance between the training sample and the virtual category.
  arXiv  Detail & Related papers  (2022-07-07T16:59:53Z)
• Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance? [14.610038284393166]
  We show that label uncertainty is explicitly embedded into the training process via distributional labels. The incorporation of label uncertainty helps the model to generalize better to unseen data and increases model performance. Similar to existing calibration methods, the distributional labels lead to better-calibrated probabilities, which in turn yield more certain and trustworthy predictions.
  arXiv  Detail & Related papers  (2022-05-30T17:19:11Z)
• DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows [52.31831255787147]
  We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA) Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution. Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
  arXiv  Detail & Related papers  (2021-05-30T22:07:13Z)

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.