SURE: Semi-dense Uncertainty-REfined Feature Matching

• URL: http://arxiv.org/abs/2603.04869v1
• Date: Thu, 05 Mar 2026 06:53:11 GMT
• Title: SURE: Semi-dense Uncertainty-REfined Feature Matching
• Abstract summary: SURE is a Semi- dense Uncertainty-REfined matching framework that jointly predicts correspondences and their confidence.<n>Our approach in- troduces a novel evidential head for trustworthy coordinate regression, along with a lightweight spatial fusion module.<n>Our method consistently outperforms existing state-of-the-art semi-dense matching models in both accuracy and efficiency.
• Score: 28.68008638977835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Establishing reliable image correspondences is essential for many robotic vision problems. However, existing methods often struggle in challenging scenarios with large viewpoint changes or textureless regions, where incorrect cor- respondences may still receive high similarity scores. This is mainly because conventional models rely solely on fea- ture similarity, lacking an explicit mechanism to estimate the reliability of predicted matches, leading to overconfident errors. To address this issue, we propose SURE, a Semi- dense Uncertainty-REfined matching framework that jointly predicts correspondences and their confidence by modeling both aleatoric and epistemic uncertainties. Our approach in- troduces a novel evidential head for trustworthy coordinate regression, along with a lightweight spatial fusion module that enhances local feature precision with minimal overhead. We evaluated our method on multiple standard benchmarks, where it consistently outperforms existing state-of-the-art semi-dense matching models in both accuracy and efficiency. our code will be available on https://github.com/LSC-ALAN/SURE.

Related papers

• Semi-Dense Matching Uncertainty Is Not Just Local Confidence [0.0]
  We propose a lightweight, post-hoc overall uncertainty estimation framework that introduces a two-component Laplace mixture model with only 9 learnable parameters.<n>The objective is to explicitly capture both the sharp local refinement noise and the broader tail of coarse-assignment failures.<n>Our method consistently improves downstream geometric accuracy across various pretrained-only matchers and robust estimators with minimal computational overhead.
  arXiv  Detail & Related papers  (2026-08-09T13:01:20Z)
• Conformal Reliability: A New Evaluation Metric for Conditional Generation [62.761166941396844]
  Conditional generative models have recently remarkable success in various applications.<n>We propose a novel evaluation metric called reliability based on prediction set at a pre-specified confidence level.<n>We introduce Conformal ReLiability (CReL), a framework that can (i) construct a prediction set and (ii) accurately optimize the reliability score within the constructed prediction set.
  arXiv  Detail & Related papers  (2026-05-29T03:52:44Z)
• Are We Overconfident in Models and Results for Semi-Supervised 3D Medical Image Segmentation? [4.120238673372104]
  Semi-supervised learning has become a dominant paradigm for reducing annotation costs.<n>We argue that the current progress is clouded by a twofold overconfidence problem.<n>We propose a tri-space principled segmentation framework founded on a dual-axis reliability assessment engine.
  arXiv  Detail & Related papers  (2026-05-25T08:16:40Z)
• Sensitivity Uncertainty Alignment in Large Language Models [0.0]
  We propose a framework for analyzing failures of large language models under adversarial and ambiguous inputs.<n>We define a scalar score, SUA_theta(x), capturing the difference between distributional sensitivity and predictive entropy.<n>We introduce SUA-TR, a training method combining consistency regularization and entropy alignment, along with an abstention rule for safer inference.
  arXiv  Detail & Related papers  (2026-04-21T17:53:12Z)
• TrustLoRA: Low-Rank Adaptation for Failure Detection under Out-of-distribution Data [62.22804234013273]
  We propose a simple failure detection framework to unify and facilitate classification with rejection under both covariate and semantic shifts.<n>Our key insight is that by separating and consolidating failure-specific reliability knowledge with low-rank adapters, we can enhance the failure detection ability effectively and flexibly.
  arXiv  Detail & Related papers  (2025-04-20T09:20:55Z)
• Distilling Calibration via Conformalized Credal Inference [30.8135853479509]
  One way to enhance reliability is through uncertainty quantification via Bayesian inference.<n>This paper introduces a low-complexity methodology to address this challenge by distilling calibration information from a more complex model.<n> Experiments on visual and language tasks demonstrate that the proposed approach, termed Conformalized Distillation for Credal Inference (CD-CI), significantly improves calibration performance.
  arXiv  Detail & Related papers  (2025-01-10T15:57:23Z)
• Revisiting Confidence Estimation: Towards Reliable Failure Prediction [53.79160907725975]
  We find a general, widely existing but actually-neglected phenomenon that most confidence estimation methods are harmful for detecting misclassification errors. We propose to enlarge the confidence gap by finding flat minima, which yields state-of-the-art failure prediction performance.
  arXiv  Detail & Related papers  (2024-03-05T11:44:14Z)
• Probabilistic Precision and Recall Towards Reliable Evaluation of Generative Models [7.770029179741429]
  We propose P-precision and P-recall (PP&PR), based on a probabilistic approach that address the problems. We show that our PP&PR provide more reliable estimates for comparing fidelity and diversity than the existing metrics.
  arXiv  Detail & Related papers  (2023-09-04T13:19:17Z)
• ELFNet: Evidential Local-global Fusion for Stereo Matching [17.675146012208124]
  We introduce the textbfEvidential textbfLocal-global textbfFusion (ELF) framework for stereo matching. It endows both uncertainty estimation and confidence-aware fusion with trustworthy heads.
  arXiv  Detail & Related papers  (2023-08-01T15:51:04Z)
• Quantifying Model Uncertainty for Semantic Segmentation using Operators in the RKHS [20.348825818435767]
  We present a framework for high-resolution predictive uncertainty quantification of semantic segmentation models. We use a multi-moment functional definition of uncertainty associated with the model's feature space in the kernel reproducing the Hilbert space (RKHS) This leads to a significantly more accurate view of model uncertainty than conventional Bayesian methods.
  arXiv  Detail & Related papers  (2022-11-03T17:10:49Z)
• Robustness and Accuracy Could Be Reconcilable by (Proper) Definition [109.62614226793833]
  The trade-off between robustness and accuracy has been widely studied in the adversarial literature. We find that it may stem from the improperly defined robust error, which imposes an inductive bias of local invariance. By definition, SCORE facilitates the reconciliation between robustness and accuracy, while still handling the worst-case uncertainty.
  arXiv  Detail & Related papers  (2022-02-21T10:36:09Z)
• PDC-Net+: Enhanced Probabilistic Dense Correspondence Network [161.76275845530964]
  Enhanced Probabilistic Dense Correspondence Network, PDC-Net+, capable of estimating accurate dense correspondences. We develop an architecture and an enhanced training strategy tailored for robust and generalizable uncertainty prediction. Our approach obtains state-of-the-art results on multiple challenging geometric matching and optical flow datasets.
  arXiv  Detail & Related papers  (2021-09-28T17:56:41Z)
• Learning Accurate Dense Correspondences and When to Trust Them [161.76275845530964]
  We aim to estimate a dense flow field relating two images, coupled with a robust pixel-wise confidence map. We develop a flexible probabilistic approach that jointly learns the flow prediction and its uncertainty. Our approach obtains state-of-the-art results on challenging geometric matching and optical flow datasets.
  arXiv  Detail & Related papers  (2021-01-05T18:54:11Z)
• Meta-Learned Confidence for Few-shot Learning [60.6086305523402]
  A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples. We propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-27T10:22:17Z)

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.