On the public citation graph

A Dynamic Multi-Scene Universal Matching Compensation Based Person Re-Identification Method for Construction Sites

https://doi.org/10.2139/ssrn.5364012
0 signed · 34 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01225-0_30openalex-snapshot
Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)
inferredcites →https://doi.org/10.1007/978-981-96-0125-7_5openalex-snapshot
Optimal Illumination Distance Metrics for Person Re-identification
inferredcites →https://doi.org/10.1007/s00521-020-04809-7openalex-snapshot
Semi-supervised person re-identification by similarity-embedded cycle GANs
inferredcites →https://doi.org/10.1007/s10462-024-10721-6openalex-snapshot
A review of convolutional neural networks in computer vision
inferredcites →https://doi.org/10.1007/s11263-019-01290-1openalex-snapshot
RGB-IR Person Re-identification by Cross-Modality Similarity Preservation
inferredcites →https://doi.org/10.1016/j.imavis.2021.104356openalex-snapshot
Spatial temporal and channel aware network for video-based person re-identification
inferredcites →https://doi.org/10.1016/j.patcog.2024.111090openalex-snapshot
MSCMNet: Multi-scale Semantic Correlation Mining for Visible-Infrared Person Re-Identification
inferredcites →https://doi.org/10.1109/cvpr.2019.00070openalex-snapshot
Dissecting Person Re-Identification From the Viewpoint of Viewpoint
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00292openalex-snapshot
Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer
inferredcites →https://doi.org/10.1109/cvpr52688.2022.00471openalex-snapshot
Feature Erasing and Diffusion Network for Occluded Person Re-Identification
inferredcites →https://doi.org/10.1109/cvpr52733.2024.02568openalex-snapshot
Noisy-Correspondence Learning for Text-to-Image Person Re-Identification
inferredcites →https://doi.org/10.1109/cvprw.2019.00190openalex-snapshot
Bag of Tricks and a Strong Baseline for Deep Person Re-Identification
inferredcites →https://doi.org/10.1109/iccv.2015.133openalex-snapshot
Scalable Person Re-identification: A Benchmark
inferredcites →https://doi.org/10.1109/iccv.2017.427openalex-snapshot
Pose-Driven Deep Convolutional Model for Person Re-identification
inferredcites →https://doi.org/10.1109/iccv.2019.00063openalex-snapshot
Pose-Guided Feature Alignment for Occluded Person Re-Identification
inferredcites →https://doi.org/10.1109/iccv.2019.00380openalex-snapshot
Omni-Scale Feature Learning for Person Re-Identification
inferredcites →https://doi.org/10.1109/iccv48922.2021.01469openalex-snapshot
ICE: Inter-instance Contrastive Encoding for Unsupervised Person Re-identification
inferredcites →https://doi.org/10.1109/tcsvt.2019.2948093openalex-snapshot
Beyond Scalar Neuron: Adopting Vector-Neuron Capsules for Long-Term Person Re-Identification
inferredcites →https://doi.org/10.1109/tifs.2025.3536608openalex-snapshot
CILP-FGDI: Exploiting Vision-Language Model for Generalizable Person Re-Identification
inferredcites →https://doi.org/10.1109/tpami.1983.4767365openalex-snapshot
A Perspective on Range Finding Techniques for Computer Vision
inferredcites →https://doi.org/10.1109/tpami.2014.2369055openalex-snapshot
Person Re-Identification by Iterative Re-Weighted Sparse Ranking
inferredcites →https://doi.org/10.1109/tpami.2019.2960509openalex-snapshot
Person Re-Identification by Contour Sketch Under Moderate Clothing Change
inferredcites →https://doi.org/10.1109/tpami.2021.3054775openalex-snapshot
Deep Learning for Person Re-Identification: A Survey and Outlook
inferredcites →https://doi.org/10.1109/tpami.2023.3334741openalex-snapshot
Identity-Guided Collaborative Learning for Cloth-Changing Person Reidentification
inferredcites →https://doi.org/10.1109/tpami.2024.3381184openalex-snapshot
A Versatile Framework for Multi-Scene Person Re-Identification
inferredcites →https://doi.org/10.1109/wacv48630.2021.00248openalex-snapshot
Learning Shape Representations for Person Re-Identification under Clothing Change
inferredcites →https://doi.org/10.1109/wacv57701.2024.00108openalex-snapshot
Contrastive Viewpoint-aware Shape Learning for Long-term Person Re-Identification
inferredcites →https://doi.org/10.1109/wacv61041.2025.00164openalex-snapshot
DLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-Id
inferredcites →https://doi.org/10.1609/aaai.v32i1.12284openalex-snapshot
Deep Low-Resolution Person Re-Identification
inferredcites →https://doi.org/10.48550/arxiv.1610.02984openalex-snapshot
Person Re-identification: Past, Present and Future
inferredcites →https://doi.org/10.48550/arxiv.1709.08325openalex-snapshot
Pose-driven Deep Convolutional Model for Person Re-identification
inferredcites →https://doi.org/10.48550/arxiv.1905.00953openalex-snapshot
Omni-Scale Feature Learning for Person Re-Identification
inferredcites →https://doi.org/10.48550/arxiv.2010.11929openalex-snapshot
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
inferredcites →https://doi.org/10.48550/arxiv.2102.04378openalex-snapshot
TransReID: Transformer-based Object Re-Identification
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.