On the public citation graph
POD: Discovering Primary Objects in Videos Based on Evolutionary Refinement of Object Recurrence, Background, and Primary Object Models
0 signed · 64 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.
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known work and none has been retracted — and get a dated CiteStamped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.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-10593-2_42openalex-snapshot
Video Object Discovery and Co-segmentation with Extremely Weak Supervision
inferredcites →https://doi.org/10.1007/978-3-319-10599-4_17openalex-snapshot
Efficient Image and Video Co-localization with Frank-Wolfe Algorithm
inferredcites →https://doi.org/10.1007/978-3-642-15555-0_21openalex-snapshot
Object Segmentation by Long Term Analysis of Point Trajectories
inferredcites →https://doi.org/10.1109/cvpr.2006.91openalex-snapshot
Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
inferredcites →https://doi.org/10.1109/cvpr.2009.5206652openalex-snapshot
Half-integrality based algorithms for cosegmentation of images
inferredcites →https://doi.org/10.1109/cvpr.2010.5539868openalex-snapshot
Discriminative clustering for image co-segmentation
inferredcites →https://doi.org/10.1109/cvpr.2011.5995415openalex-snapshot
From co-saliency to co-segmentation: An efficient and fully unsupervised energy minimization model
inferredcites →https://doi.org/10.1109/cvpr.2012.6247728openalex-snapshot
Higher order motion models and spectral clustering
inferredcites →https://doi.org/10.1109/cvpr.2012.6247735openalex-snapshot
Maximum weight cliques with mutex constraints for video object segmentation
inferredcites →https://doi.org/10.1109/cvpr.2012.6247745openalex-snapshot
Unsupervised co-segmentation through region matching
inferredcites →https://doi.org/10.1109/cvpr.2012.6248065openalex-snapshot
Learning object class detectors from weakly annotated video
inferredcites →https://doi.org/10.1109/cvpr.2013.210openalex-snapshot
Topical Video Object Discovery from Key Frames by Modeling Word Co-occurrence Prior
inferredcites →https://doi.org/10.1109/cvpr.2013.253openalex-snapshot
Unsupervised Joint Object Discovery and Segmentation in Internet Images
inferredcites →https://doi.org/10.1109/cvpr.2013.87openalex-snapshot
Video Object Segmentation through Spatially Accurate and Temporally Dense Extraction of Primary Object Regions
inferredcites →https://doi.org/10.1109/cvpr.2014.190openalex-snapshot
Co-localization in Real-World Images
inferredcites →https://doi.org/10.1109/cvpr.2014.261openalex-snapshot
Enriching Visual Knowledge Bases via Object Discovery and Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2015.7298724openalex-snapshot
Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals
inferredcites →https://doi.org/10.1109/cvpr.2015.7298961openalex-snapshot
Saliency-aware geodesic video object segmentation
inferredcites →https://doi.org/10.1109/cvpr.2015.7299008openalex-snapshot
Multiple random walkers and their application to image cosegmentation
inferredcites →https://doi.org/10.1109/cvpr.2015.7299055openalex-snapshot
Causal video object segmentation from persistence of occlusions
inferredcites →https://doi.org/10.1109/cvpr.2015.7299114openalex-snapshot
Superpixel-based video object segmentation using perceptual organization and location prior
inferredcites →https://doi.org/10.1109/iccv.1998.710861openalex-snapshot
Motion segmentation and tracking using normalized cuts
inferredcites →https://doi.org/10.1109/iccv.2009.5459261openalex-snapshot
An efficient algorithm for Co-segmentation
inferredcites →https://doi.org/10.1109/iccv.2011.6126239openalex-snapshot
Distributed cosegmentation via submodular optimization on anisotropic diffusion
inferredcites →https://doi.org/10.1109/iccv.2011.6126418openalex-snapshot
Object segmentation in video: A hierarchical variational approach for turning point trajectories into dense regions
inferredcites →https://doi.org/10.1109/iccv.2011.6126471openalex-snapshot
Key-segments for video object segmentation
inferredcites →https://doi.org/10.1109/iccv.2013.110openalex-snapshot
Image Co-segmentation via Consistent Functional Maps
inferredcites →https://doi.org/10.1109/iccv.2013.223openalex-snapshot
Fast Object Segmentation in Unconstrained Video
inferredcites →https://doi.org/10.1109/iccv.2013.273openalex-snapshot
Video Segmentation by Tracking Many Figure-Ground Segments
inferredcites →https://doi.org/10.1109/tpami.2012.120openalex-snapshot
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
inferredcites →https://doi.org/10.1109/tpami.2012.28openalex-snapshot
Measuring the Objectness of Image Windows
inferredcites →https://doi.org/10.7551/mitpress/1120.003.0082openalex-snapshot
Latent Dirichlet Allocation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-030-01264-9_32openalex-snapshot
Sequential Clique Optimization for Video Object Segmentation
inferred← citeshttps://doi.org/10.1007/978-3-030-42128-1_5openalex-snapshot
Unsupervised Learning of Object Segmentation in Video with Highly Probable Positive Features
inferred← citeshttps://doi.org/10.1007/978-3-030-42128-1_6openalex-snapshot
Coupling Appearance and Motion: Unsupervised Clustering for Object Segmentation Through Space and Time
inferred← citeshttps://doi.org/10.1007/978-3-030-42128-1_7openalex-snapshot
Unsupervised Learning in Space and Time over Several Generations of Teacher and Student Networks
inferred← citeshttps://doi.org/10.1007/978-3-030-58520-4_18openalex-snapshot
Interactive Video Object Segmentation Using Global and Local Transfer Modules
inferred← citeshttps://doi.org/10.1007/978-3-031-71602-7_17openalex-snapshot
Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos
inferred← citeshttps://doi.org/10.1007/978-3-031-91838-4_23openalex-snapshot
Closer to Ground Truth: Realistic Shape and Appearance Labeled Data Generation for Unsupervised Underwater Image Segmentation
inferred← citeshttps://doi.org/10.1007/s11263-019-01183-3openalex-snapshot
Unsupervised Learning of Foreground Object Segmentation
inferred← citeshttps://doi.org/10.1016/j.ijleo.2020.166251openalex-snapshot
Efficient foreground object segmentation from video by Probability Weighted Moments
inferred← citeshttps://doi.org/10.1080/02564602.2018.1454347openalex-snapshot
ESUMM: Event SUMMarization on Scale-Free Networks
inferred← citeshttps://doi.org/10.1109/access.2019.2933479openalex-snapshot
Online Semantic Object Segmentation for Vision Robot Collected Video
inferred← citeshttps://doi.org/10.1109/cvpr.2017.784openalex-snapshot
Primary Object Segmentation in Videos Based on Region Augmentation and Reduction
inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.00724openalex-snapshot
Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps
inferred← citeshttps://doi.org/10.1109/iccv.2017.465openalex-snapshot
Unsupervised Learning from Video to Detect Foreground Objects in Single Images
inferred← citeshttps://doi.org/10.1109/iccv.2017.544openalex-snapshot
Unsupervised Object Segmentation in Video by Efficient Selection of Highly Probable Positive Features
inferred← citeshttps://doi.org/10.1109/tcsvt.2018.2870832openalex-snapshot
Review of Visual Saliency Detection With Comprehensive Information
inferred← citeshttps://doi.org/10.1109/tcsvt.2022.3188699openalex-snapshot
AppFuse: An Appearance Fusion Framework for Saliency Cues
inferred← citeshttps://doi.org/10.1109/tip.2017.2736418openalex-snapshot
Unsupervised Primary Object Discovery in Videos Based on Evolutionary Primary Object Modeling With Reliable Object Proposals
inferred← citeshttps://doi.org/10.1109/wacv56688.2023.00022openalex-snapshot
TCAM: Temporal Class Activation Maps for Object Localization in Weakly-Labeled Unconstrained Videos
inferred← citeshttps://doi.org/10.1109/wacv61041.2025.00778openalex-snapshot
Disentangling Spatio-Temporal Knowledge for Weakly Supervised Object Detection and Segmentation in Surgical Video
inferred← citeshttps://doi.org/10.1117/12.2303460openalex-snapshot
An unsupervised video foreground co-localization and segmentation process by incorporating motion cues and frame features
inferred← citeshttps://doi.org/10.3390/electronics11040639openalex-snapshot
Image Segmentation from Sparse Decomposition with a Pretrained Object-Detection Network
inferred← citeshttps://doi.org/10.3390/electronics11182899openalex-snapshot
Sequential Clique Optimization for Unsupervised and Weakly Supervised Video Object Segmentation
inferred← citeshttps://doi.org/10.3390/sym13010038openalex-snapshot
Unsupervised Learning from Videos for Object Discovery in Single Images
inferred← citeshttps://doi.org/10.4018/ijmdem.2018100102openalex-snapshot
Unsupervised Video Object Foreground Segmentation and Co-Localization by Combining Motion Boundaries and Actual Frame Edges
inferred← citeshttps://doi.org/10.48550/arxiv.2003.09556openalex-snapshot
Appearance Fusion of Multiple Cues for Video Co-localization
inferred← citeshttps://doi.org/10.48550/arxiv.2104.10386openalex-snapshot
Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps
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.