Hierarchical Motion Field Alignment for Robust Optical Flow Estimation.

Dianbo Ma, Kousuke Imamura, Ziyan Gao, Xiangjie Wang, Satoshi Yamane

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Dianbo MaGraduate School of Natural Science and Technology, Kanazawa University, Kanazawa 9201192, Japan.ORCID 0009-0006-9149-3315

Kousuke ImamuraGraduate School of Natural Science and Technology, Kanazawa University, Kanazawa 9201192, Japan.

Ziyan GaoSchool of Information Science, Japan Advanced Institute of Science and Technology, Nomi 9231292, Japan.

Xiangjie WangState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China.ORCID 0000-0002-7627-2374

Satoshi YamaneGraduate School of Natural Science and Technology, Kanazawa University, Kanazawa 9201192, Japan.ORCID 0000-0001-7883-4054

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Optical flow estimation is a fundamental and long-standing task in computer vision, facilitating the understanding of motion within visual scenes. In this study, we aim to improve optical flow estimation, particularly in challenging scenarios involving small and fast-moving objects. Specifically, we proposed a learning-based model incorporating two key components: the Hierarchical Motion Field Alignment module, which ensures accurate estimation of objects of varying sizes while maintaining manageable computational complexity, and the Correlation Self-Attention module, which effectively handles large displacements, making the model suitable for scenarios with fast-moving objects. Additionally, we introduced a Multi-Scale Correlation Search layer to enhance the four-dimensional cost volume, enabling the model to address various types of motion. Experimental results demonstrate that our model achieves superior generalization performance and significantly improves the estimation of small, fast-moving objects.

Indexed as

attention mechanismscomputer visioncorrelationdeep learningimage processingmotion estimationoptical flowrecurrent neural networksresidual neural networkssupervised learning

Identifiers

PMID40363093

DOI10.3390/s25092653

PMCPMC12074433

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.