The paper introduces Local Adaptive Probability Estimation (LAPE), a post‑hoc residual calibration framework that refines global calibrator outputs using non‑parametric local evidence from neighboring samples in feature space. LAPE serves as a plug‑and‑play module that leverages feature‑space geometry and reuses calibration data, improving calibration quality for various baselines. The authors also extend the approach to multi‑view inputs with Cascade Feature Augmentation Fusion (CFAF) and demonstrate its effectiveness through extensive experiments.
arXiv:2605. 30188v2 Announce Type: replace-cross Abstract: Reliable probability estimates are critical in many machine learning applications, yet modern classifiers are often poorly calibrated.
arXiv:2606. 28654v1 Announce Type: cross Abstract: Deep Neural Network (DNN) classifiers suffer from poor calibration when their softmax outputs (predictive confidence) deviate from the empirical likelihoods.
The paper introduces Learning Neighbor Regions (LNR), a coarse‑to‑fine framework for robust multi‑model fitting in noisy environments. LNR’s coarse module uses a neural network to analyze geometric features of minimum sets, selecting a small number of promising hypotheses to improve efficiency. The fine module encodes neighbor region features for each hypothesis, allowing simultaneous refinement and scoring while avoiding differentiation of the sampling process and solvers. Experiments on four classic tasks show state‑of‑the‑art performance and suggest easy adaptation to other robust fitting problems.
The paper introduces SMART, a lightweight recalibration technique that adjusts logits based on the margin between the top two logits, called the logit gap. It uses a soft-binned Expected Calibration Error objective to balance bias and variance, enabling stable updates even with limited calibration data. Experiments across various datasets and architectures show SMART achieves state‑of‑the‑art calibration with fewer parameters than existing methods.
The paper introduces EUA‑Cal, a calibration method that preserves the early‑training uncertainty of deep neural networks. By regularizing predictions and feature prototypes based on the early model, EUA‑Cal mitigates overconfidence while maintaining accuracy. Experiments on image classification and multiple‑choice question answering across eight models show EUA‑Cal outperforms existing calibration techniques.
arXiv:2608.30230v1 Announce Type: new Abstract: Test-time prompt tuning (TPT) has emerged as a powerful paradigm, refining prompts for each test sample via entropy minimization (EM) over multiple aug...
arXiv:2610. 09020v1 Announce Type: new Abstract: Modern neural networks are often miscalibrated, with a tendency to overconfidence.
arXiv:2607. 07745v1 Announce Type: new Abstract: While accuracy, robustness, and calibration are all essential for reliable neural networks, they are often studied separately; developing models that satisfy all three simultaneously remains a central challenge.
arXiv:2610.27547v1 Announce Type: new Abstract: Online LiDAR-camera calibration remains a fundamental yet challenging task in the field of robotics. Recently, deep learning-based data-driven approach...
arXiv:2609.21522v1 Announce Type: new Abstract: Recent pre-trained foundation models provide rich multi-modal priors for downstream 3D vision tasks. However, the effectiveness of these representation...
arXiv:2609.17386v1 Announce Type: new Abstract: Test-time prompt tuning (TPT) enables adaptation on a single test instance, achieving improved accuracy but often sacrificing calibration performance....
arXiv:2608.20788v1 Announce Type: new Abstract: Deep learning-based Multi-View Stereo (MVS) has advanced significantly but often generalizes poorly to unseen scenes, particularly in occluded areas or...