Data Association
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Related Articles from SNS
SODA-CitrON: Static Object Data Association by Clustering Multi-Modal Sensor Detections Online
arXiv:2602.22243v3 Announce Type: replace Abstract: The online fusion and tracking of static objects from heterogeneous sensor detections is a fundamental problem in robotics, autonomous systems, and environmental mapping. Although classical data association approaches such as JPDA are well suited for dynamic targets, they are less effective for static objects observed intermittently and with heterogeneous uncertainties, where motion models provide minimal discriminative power with respect...
BPDA-GMM: Bayesian Probabilistic Data Association via Gaussian Mixture Models for Semantic SLAM
arXiv:2606.04618v1 Announce Type: new Abstract: Probabilistic data association (PDA) improves semantic SLAM in perceptually aliased scenes, but existing methods often assume a fixed landmark set, recompute association weights as the map grows, or rely on hand-tuned null-hypothesis weights. To address these limitations, we propose \textbf{BPDA-GMM}, an online Bayesian PDA framework for semantic SLAM with a growing object-level map. BPDA-GMM uses a Dirichlet-process prior to induce a Chinese...
Seg2Track++: Probabilistic Track Validation and Data Association for Multi-Object Tracking and Segmentation
Announce Type: new Abstract: Autonomous systems require robust Multi-Object Tracking and Segmentation (MOTS) to operate reliably in dynamic environments, ensuring consistent object identities and precise mask-level delineation. Foundation models such as SAM2 have shown strong zero-shot generalization for segmentation, but their direct application to MOTS is limited by unreliable track association and false-positive propagation. This work introduces Seg2Track++, a framework that integrates...
An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)
Announce Type: new Abstract : Purpose: To develop an interpretable and trustworthy AI framework that combines deep learning based MRI Osteoarthritis Knee Score (MOAKS) prediction with interpretable statistical modeling to study structure-pain relationships at scale using data from the Osteoarthritis Initiative (OAI). Materials and Methods: We first developed a deep learning framework to predict MOAKS features directly from knee MRIs and incorporated conformal prediction to provide prediction...
Non-association independent schools inspections and outcomes: management information
Non-association independent schools inspections and outcomes: management information Management information aggregating in-year and most recent inspections and outcomes. - From: - Ofsted - Published - 20 February 2018 - Last updated - 9 June 2026 — See all updates For the latest individual inspection reports, please visit our reports website.
Inferring Events from Time Series using Language Models
arXiv:2503.14190v3 Announce Type: replace Abstract: A common goal in analyzing time series data is to understand how events cause observed variations. We study whether Large Language Models (LLMs) can infer natural language events associated with time series data. We introduce an automated method for generating tasks that test a model's ability to reason about events associated with time series data based on sports data, and develop a new benchmarking method.
Adult social care provider statistics, England: quarterly update to May 2026
Adult social care provider statistics, England: quarterly update to May 2026 Official statistics on the number of people receiving adult social care services, and information on adult social care settings. Applies to England Documents Details Official statistics on adult social care in England. This publication consists of: - a quarterly report - associated data tables Data on adult social care settings covered in this publication includes: - occupancy levels in care homes - number of care...
Model Recycling Framework for Multi-Source Data-Free Supervised Transfer Learning
Announce Type: replace Abstract: Increasing concerns for data privacy and other difficulties associated with retrieving source data for model training have created the need for source-free transfer learning, in which one only has access to pre-trained models instead of data from the original source domains. This setting introduces many challenges, as many existing transfer learning methods typically rely on access to source data, which limits their direct applicability to scenarios where...
Learning Association via Track-Detection Matching for Multi-Object Tracking
arXiv:2512.22105v2 Announce Type: replace Abstract: Multi-object tracking aims to maintain object identities over time by associating detections across video frames. Two dominant paradigms exist in literature: tracking-by-detection methods, which are computationally efficient but rely on handcrafted association heuristics, and end-to-end approaches, which learn association from data at the cost of higher computational complexity. We propose Track-Detection Link Prediction (TDLP), a...
Hidden geometry explains why kernel methods separate complex data so well
Hidden geometry explains why kernel methods separate complex data so well Lisa Lock Scientific Editor Robert Egan Associate Editor Are two sets of data genuinely different, or is it because of randomness? This question, known as the two-sample testing problem, becomes notoriously difficult in modern datasets, because they are often high-dimensional, complex, and differences between them can take countless subtle forms. "Simply put, we don't know what differences to look for, the...