SOTAVerified

Boundary Detection

Boundary Detection is a vital part of extracting information encoded in images, allowing for the computation of quantities of interest including density, velocity, pressure, etc.

Source: A Locally Adapting Technique for Boundary Detection using Image Segmentation

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–175 of 359 papers

TitleStatusHype
Winning the CVPR'2021 Kinetics-GEBD Challenge: Contrastive Learning ApproachCode1
Discerning Generic Event Boundaries in Long-Form Wild Videos—0
Advanced Hough-based method for on-device document localizationCode0
The Oxford Road Boundaries Dataset—0
ICDAR 2021 Competition on Components Segmentation Task of Document Photos—0
Interior point search for nonparametric image segmentation—0
Weighting vectors for machine learning: numerical harmonic analysis applied to boundary detection—0
Rethinking Boundaries: End-To-End Recognition of Discontinuous Mentions with Pointer NetworksCode0
Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extractionwith Rich Syntactic KnowledgeCode1
Capturing Logical Structure of Visually Structured Documents with Multimodal Transition ParserCode1
Shot Contrastive Self-Supervised Learning for Scene Boundary Detection—0
Visual Saliency TransformerCode1
Neuro-inspired edge feature fusion using Choquet integralsCode0
A Multi-Task Deep Learning Framework for Building Footprint SegmentationCode1
Continual Learning with Fully Probabilistic Models—0
Learning structure-aware semantic segmentation with image-level supervisionCode0
Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images—0
Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images for Autonomous DrivingCode1
DynOcc: Learning Single-View Depth from Dynamic Occlusion Cues—0
Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayersCode1
PcmNet: Position-Sensitive Context Modeling Network for Temporal Action Localization—0
A Practical Framework for ROI Detection in Medical Images -- a case study for hip detection in anteroposterior pelvic radiographs—0
A Context-Enhanced De-identification System—0
Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection—0
Learning Crisp Boundaries Using Deep Refinement Network and Adaptive Weighting Loss—0
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All datasetsRoFTRoFT-chatgptCoAuthorNYU-Depth V2TriBERT (in-domain)UruDendroKinetics 400PASCAL Context

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GigaCheck (DN-DAB-DETR)Accuracy (%)64.63—Unverified
2RoBERTa + SEPAccuracy (%)49.64—Unverified
3PHD + TS MLAccuracy (%)23.5—Unverified
4TLE + TS BinaryAccuracy (%)12.58—Unverified
#ModelMetricClaimedVerifiedStatus
1GigaCheck (DN-DAB-DETR)Accuracy (%)67.65—Unverified
2RoBERTa + SEPAccuracy (%)54.61—Unverified
3TLE + TS BinaryAccuracy (%)20.02—Unverified
4PHD + TS MLAccuracy (%)17.29—Unverified
#ModelMetricClaimedVerifiedStatus
1GigaCheck (Mistral-7B-v0.3)Cohen’s Kappa score0.42—Unverified
2DeBERTa-v3 (Naive)Cohen’s Kappa score0.4—Unverified
3GigaCheck (DN-DAB-DETR)Cohen’s Kappa score0.19—Unverified
#ModelMetricClaimedVerifiedStatus
1InvPTodsF78.1—Unverified
2PGT (Swin-S)odsF78.04—Unverified
3PGT (Swin-T)odsF77.05—Unverified
#ModelMetricClaimedVerifiedStatus
1GigaCheck (DN-DAB-DETR)F1@30.65—Unverified
2TriBERT (p=2)F1@30.58—Unverified
#ModelMetricClaimedVerifiedStatus
1CS-TRDAverage Precision0.94—Unverified
2INBDAverage Precision0.75—Unverified
#ModelMetricClaimedVerifiedStatus
1CASTANET+ EnsemblePairwise F10.81—Unverified
#ModelMetricClaimedVerifiedStatus
1InvPTodsF73—Unverified