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ARXIV:2603.21629 · MULTI-OBJECT TRACKING · SUBMITTED 24 MAR · 21:26 UTC · FRESHNESS STALE
ARXIV:2603.21629MULTI-OBJECT TRACKINGSUBMITTED 24 MAR · 21:26 UTCFRESHNESS STALEWen Guo · Pengfei Zhao · Zongmeng Wang · Yufan Hu · Junyu Gao · arXiv
A test-time adaptation framework for multi-object tracking that leverages memory and experience to improve performance under distribution shifts.
Opportunity summary
Pain A test-time adaptation framework for multi-object tracking that leverages memory and experience to improve performance under distribution shifts.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence unverified
A test-time adaptation framework for multi-object tracking that leverages memory and experience to improve performance under distribution shifts. However, due to distribution shifts in appearance, motion pattern, and catagory between the training and testing…
Multiple Object Tracking (MOT) has long been a fundamental task in computer vision, with broad applications in various real-world scenarios. However, due to distribution shifts in appearance, motion pattern, and catagory between the training…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. However, existing TTA methods often fail to deliver satisfactory results in MOT, as they primarily focus solely on frame-level adaptation while neglecting temporal consistency…
Multi-Object Tracking moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A test-time adaptation framework for multi-object tracking that leverages memory and experience to improve performance under distribution shifts.
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10.48550/arXiv.2603.21629A test-time adaptation framework for multi-object tracking that leverages memory and experience to improve performance under distribution shifts.
Abstract
Multiple Object Tracking (MOT) has long been a fundamental task in computer vision, with broad applications in various real-world scenarios. However, due to distribution shifts in appearance, motion pattern, and catagory between the training and testing data, model performance degrades considerably during online inference in MOT. Test-Time Adaptation (TTA) has emerged as a promising paradigm to alleviate such distribution shifts. However, existing TTA methods often fail to deliver satisfactory results in MOT, as they primarily focus solely on frame-level adaptation while neglecting temporal consistency and identity association across frames and videos. Inspired by human decision-making process, this paper propose a Test-time Calibration from Experience and Intuition (TCEI) framework. In this framework, the Intuitive system utilizes transient memory to recall recently observed objects for rapid predictions, while the Experiential system leverages the accumulated experience from prior test videos to reassess and calibrate these intuitive predictions. Furthermore, both confident and uncertain objects during online testing are exploited as historical priors and reflective cases, respectively, enabling the model to adapt to the testing environment and alleviate performance degradation. Extensive experiments demonstrate that the proposed TCEI framework consistently achieves superior performance across multiple benchmark datasets and significantly enhances the model's adaptability under distribution shifts. The code will be released at https://github.com/1941Zpf/TCEI.
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PROBLEM
A test-time adaptation framework for multi-object tracking that leverages memory and experience to improve performance under distribution shifts. However, due to distribution shifts in appearance, motion pattern, and catagory between the training and testing data, model performa...
METHOD
Multiple Object Tracking (MOT) has long been a fundamental task in computer vision, with broad applications in various real-world scenarios. However, due to distribution shifts in appearance, motion pattern, and catagory between the training and testing data, model performance d...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. However, existing TTA methods often fail to deliver satisfactory results in MOT, as they primarily focus solely on frame-level adaptation while neglecting temporal consistency and identity association acr...
WHY NOW
Multi-Object Tracking moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A test-time adaptation framework for multi-object tracking that leverages memory and experience to improve performance under distribution shifts. However, due to distribution shifts in appearance, motion pattern, and catagory between the training and testing data, model performance degrades considerably during online inference in MOT.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multiple Object Tracking (MOT) has long been a fundamental task in computer vision, with broad applications in various real-world scenarios. However, due to distribution shifts in appearance, motion pattern, and catagory between the training and testing data, model performance degrades considerably during online inference in MOT.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. However, existing TTA methods often fail to deliver satisfactory results in MOT, as they primarily focus solely on frame-level adaptation while neglecting temporal consistency and identity association across frames and videos. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multi-Object Tracking moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A test-time adaptation framework for multi-object tracking that leverages memory and experience to improve performance under distribution shifts.
Segment
Multi-Object Tracking
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7.0/10 public viability
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