PlugSI: Plug-and-Play Test-Time Graph Adaptation for Spatial Interpolation

• URL: http://arxiv.org/abs/2602.09824v1
• Date: Tue, 10 Feb 2026 14:33:23 GMT
• Title: PlugSI: Plug-and-Play Test-Time Graph Adaptation for Spatial Interpolation
• Authors: Xuhang Wu, Zhuoxuan Liang, Wei Li, Xiaohua Jia, Sumi Helal,
• Abstract summary: We propose PlugSI, a plug-and-play framework that refines test-time graph through two key innovations.<n>First, we design an Unknown Topology Adapter (UTA) that adapts to the new graph structure of each small-batch at test-time.<n>Second, we introduce a Temporal Balance Adapter (TBA) that maintains a stable historical consensus to guide UTA adaptation.
• Score: 21.615719024977537
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid advancement of IoT and edge computing, sensor networks have become indispensable, driving the need for large-scale sensor deployment. However, the high deployment cost hinders their scalability. To tackle the issues, Spatial Interpolation (SI) introduces virtual sensors to infer readings from observed sensors, leveraging graph structure. However, current graph-based SI methods rely on pre-trained models, lack adaptation to larger and unseen graphs at test-time, and overlook test data utilization. To address these issues, we propose PlugSI, a plug-and-play framework that refines test-time graph through two key innovations. First, we design an Unknown Topology Adapter (UTA) that adapts to the new graph structure of each small-batch at test-time, enhancing the generalization of SI pre-trained models. Second, we introduce a Temporal Balance Adapter (TBA) that maintains a stable historical consensus to guide UTA adaptation and prevent drifting caused by noise in the current batch. Empirically, extensive experiments demonstrate PlugSI can be seamlessly integrated into existing graph-based SI methods and provide significant improvement (e.g., a 10.81% reduction in MAE).

Related papers

• Lightweight Test-Time Adaptation for EMG-Based Gesture Recognition [2.414036142474149]
  We propose a lightweight framework for Test-Time Adaptation (TTA) using a Temporal Convolutional Network (TCN) backbone.<n>We introduce three deployment-ready strategies: causal adaptive batch normalization for real-time statistical alignment; (ii) a Gaussian Mixture Model (GMM) alignment with experience replay to prevent forgetting; and (iii) meta-learning for rapid, few-shot calibration.<n>Our results show that experience-replay updates yield superior stability under limited data, while meta-learning achieves competitive performance in one- and two-shot regimes.
  arXiv  Detail & Related papers  (2026-01-07T18:48:31Z)
• Enhancing Test Time Adaptation with Few-shot Guidance [62.49199492255226]
  Deep neural networks often encounter significant performance drops while facing with domain shifts between training (source) and test (target) data.<n>Test Time Adaptation (TTA) methods have been proposed to adapt pre-trained source model to handle out-of-distribution streaming target data.<n>We develop Few-Shot Test Time Adaptation (FS-TTA), a novel and practical setting that utilizes a few-shot support set on top of TTA.
  arXiv  Detail & Related papers  (2024-09-02T15:50:48Z)
• Test-Time Model Adaptation with Only Forward Passes [68.11784295706995]
  Test-time adaptation has proven effective in adapting a given trained model to unseen test samples with potential distribution shifts. We propose a test-time Forward-Optimization Adaptation (FOA) method. FOA runs on quantized 8-bit ViT, outperforms gradient-based TENT on full-precision 32-bit ViT, and achieves an up to 24-fold memory reduction on ImageNet-C.
  arXiv  Detail & Related papers  (2024-04-02T05:34:33Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments [2.7776688429637466]
  FLARE is a lightweight dual-scheduler FL framework that conditionally transfers training data and deploys models between edge and sensor endpoints. We show that FLARE can significantly reduce the amount of data exchanged between edge and sensor nodes compared to fixed-interval scheduling methods. It can successfully detect concept drift reactively with at least a 16x reduction in latency.
  arXiv  Detail & Related papers  (2023-05-15T10:09:07Z)
• Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical Systems [17.373668215331737]
  We propose a new approach called deep graph vector data description (SVDD) for anomaly detection. We first use a transformer to preserve both short and long temporal patterns monitoring data in temporal embeddings. We cluster these embeddings according to sensor type and utilize them to estimate the change in connectivity between various sensors to construct a new weighted graph.
  arXiv  Detail & Related papers  (2023-02-24T22:14:39Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Online Graph Topology Learning from Matrix-valued Time Series [0.0]
  The focus is on the statistical analysis of matrix-valued time series, where data is collected over a network of sensors. The goal is to identify the dependency structure among these sensors and represent it with a graph. Online algorithms are adapted to these augmented data models, allowing for simultaneous learning of the graph and trend from streaming samples.
  arXiv  Detail & Related papers  (2021-07-16T17:21:14Z)
• AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting [14.797761571844893]
  Time series of sensor values is crucial in urban applications such as air pollution alert, biking resource management, and intelligent transportation systems. Recent advances exploit graph neural networks (GNN) to better learn spatial and temporal dependencies between sensors.
  arXiv  Detail & Related papers  (2021-01-29T08:31:38Z)
• SADet: Learning An Efficient and Accurate Pedestrian Detector [68.66857832440897]
  This paper proposes a series of systematic optimization strategies for the detection pipeline of one-stage detector. It forms a single shot anchor-based detector (SADet) for efficient and accurate pedestrian detection. Though structurally simple, it presents state-of-the-art result and real-time speed of $20$ FPS for VGA-resolution images.
  arXiv  Detail & Related papers  (2020-07-26T12:32:38Z)
• Deep Soft Procrustes for Markerless Volumetric Sensor Alignment [81.13055566952221]
  In this work, we improve markerless data-driven correspondence estimation to achieve more robust multi-sensor spatial alignment. We incorporate geometric constraints in an end-to-end manner into a typical segmentation based model and bridge the intermediate dense classification task with the targeted pose estimation one. Our model is experimentally shown to achieve similar results with marker-based methods and outperform the markerless ones, while also being robust to the pose variations of the calibration structure.
  arXiv  Detail & Related papers  (2020-03-23T10:51:32Z)

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.