PAID: Pairwise Angular-Invariant Decomposition for Continual Test-Time Adaptation

• URL: http://arxiv.org/abs/2506.02453v1
• Date: Tue, 03 Jun 2025 05:18:15 GMT
• Title: PAID: Pairwise Angular-Invariant Decomposition for Continual Test-Time Adaptation
• Authors: Kunyu Wang, Xueyang Fu, Yunfei Bao, Chengjie Ge, Chengzhi Cao, Wei Zhai, Zheng-Jun Zha,
• Abstract summary: This paper takes the geometric attributes of pre-trained weights as a starting point, systematically analyzing three key components: magnitude, absolute angle, and pairwise angular structure.<n>We find that the pairwise angular structure remains stable across diverse corrupted domains and encodes domain-invariant semantic information, suggesting it should be preserved during adaptation.
• Score: 71.15558243612227
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Continual Test-Time Adaptation (CTTA) aims to online adapt a pre-trained model to changing environments during inference. Most existing methods focus on exploiting target data, while overlooking another crucial source of information, the pre-trained weights, which encode underutilized domain-invariant priors. This paper takes the geometric attributes of pre-trained weights as a starting point, systematically analyzing three key components: magnitude, absolute angle, and pairwise angular structure. We find that the pairwise angular structure remains stable across diverse corrupted domains and encodes domain-invariant semantic information, suggesting it should be preserved during adaptation. Based on this insight, we propose PAID (Pairwise Angular-Invariant Decomposition), a prior-driven CTTA method that decomposes weight into magnitude and direction, and introduces a learnable orthogonal matrix via Householder reflections to globally rotate direction while preserving the pairwise angular structure. During adaptation, only the magnitudes and the orthogonal matrices are updated. PAID achieves consistent improvements over recent SOTA methods on four widely used CTTA benchmarks, demonstrating that preserving pairwise angular structure offers a simple yet effective principle for CTTA.

Related papers

• 3-Dimensional CryoEM Pose Estimation and Shift Correction Pipeline [2.009945677846956]
  Accurate pose estimation and shift correction are key challenges in cryo-EM due to the very low SNR, which directly impacts the fidelity of 3D reconstructions.<n>We present an approach for pose estimation in cryo-EM that leverages multi-dimensional scaling (MDS) techniques in a robust manner to estimate the 3D rotation matrix of each particle from pairs of dihedral angles.
  arXiv  Detail & Related papers  (2025-07-20T11:46:17Z)
• CP$^2$: Leveraging Geometry for Conformal Prediction via Canonicalization [51.716834831684004]
  We study the problem of conformal prediction (CP) under geometric data shifts.<n>We propose integrating geometric information--such as geometric pose--into the conformal procedure to reinstate its guarantees.
  arXiv  Detail & Related papers  (2025-06-19T10:12:02Z)
• Improving Out-of-Distribution Detection via Dynamic Covariance Calibration [12.001290283557466]
  Out-of-Distribution (OOD) detection is essential for the trustworthiness of AI systems.<n>We argue that the influence of ill-distributed samples can be corrected by dynamically adjusting the prior geometry.<n>Our approach significantly enhances OOD detection across various models.
  arXiv  Detail & Related papers  (2025-06-11T05:05:26Z)
• Optimal Transport-Based Domain Adaptation for Rotated Linear Regression [0.0]
  We focus on a supervised DA problem involving linear regression models under rotational shifts.<n>This work considers cases where source and target domains are related by a rotation-common in applications like sensor calibration or image orientation.<n>We propose an algorithm that combines K-means clustering, OT, and singular value decomposition (SVD) to estimate the rotation angle and adapt the regression model.
  arXiv  Detail & Related papers  (2025-05-14T09:06:40Z)
• A Recipe for Geometry-Aware 3D Mesh Transformers [2.0992612407358293]
  We study an approach for embedding features at the patch level, accommodating patches with variable node counts. Our research highlights critical insights: 1) the importance of structural and positional embeddings facilitated by heat diffusion in general 3D mesh transformers; 2) the effectiveness of novel components such as geodesic masking and feature interaction via cross-attention in enhancing learning; and 3) the superior performance and efficiency of our proposed methods in challenging segmentation and classification tasks.
  arXiv  Detail & Related papers  (2024-10-31T19:13:31Z)
• EqNIO: Subequivariant Neural Inertial Odometry [33.96552018734359]
  We show that IMU data transforms equivariantly, when rotated around the gravity vector and reflected with respect to arbitrary planes parallel to gravity. We then map the IMU data into this frame, thereby achieving an invariant canonicalization that can be directly used with off-the-shelf inertial odometry networks.
  arXiv  Detail & Related papers  (2024-08-12T17:42:46Z)
• Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers [59.0181939916084]
  Traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries. We propose a novel Priors Distillation (RPD) method to extract priors from the well-trained transformers on massive images. Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification.
  arXiv  Detail & Related papers  (2024-07-26T06:29:09Z)
• Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation [32.371755315509574]
  Householder reflection adaptation (HRA) is a simple but effective adaptation method based on Householder reflections. HRA achieves superior performance with fewer learnable parameters when adapting large language models and conditional image generators.
  arXiv  Detail & Related papers  (2024-05-24T16:18:16Z)
• Equivariant Deep Weight Space Alignment [54.65847470115314]
  We propose a novel framework aimed at learning to solve the weight alignment problem. We first prove that weight alignment adheres to two fundamental symmetries and then, propose a deep architecture that respects these symmetries.
  arXiv  Detail & Related papers  (2023-10-20T10:12:06Z)
• Change Point Detection with Conceptors [0.6526824510982799]
  offline change point detection retrospectively locates change points in a time series. Many nonparametric methods that target i.i.d. mean and variance changes fail in the presence of nonlinear temporal dependence. We propose use of a conceptor matrix to learn the characteristic dynamics of a baseline training window with arbitrary dependence structure. The associated echo state network acts as a featurizer of the data, and change points are identified from the nature of the interactions between the features and their relationship to the baseline state.
  arXiv  Detail & Related papers  (2023-08-11T16:32:00Z)
• A Model for Multi-View Residual Covariances based on Perspective Deformation [88.21738020902411]
  We derive a model for the covariance of the visual residuals in multi-view SfM, odometry and SLAM setups. We validate our model with synthetic and real data and integrate it into photometric and feature-based Bundle Adjustment.
  arXiv  Detail & Related papers  (2022-02-01T21:21:56Z)
• Frame Averaging for Equivariant Shape Space Learning [85.42901997467754]
  A natural way to incorporate symmetries in shape space learning is to ask that the mapping to the shape space (encoder) and mapping from the shape space (decoder) are equivariant to the relevant symmetries. We present a framework for incorporating equivariance in encoders and decoders by introducing two contributions.
  arXiv  Detail & Related papers  (2021-12-03T06:41:19Z)

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.