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

Showing 1–25 of 359 papers

TitleStatusHype
SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation—0
Design and Implementation of an OCR-Powered Pipeline for Table Extraction from Invoices—0
Real Time Self-Tuning Adaptive Controllers on Temperature Control Loops using Event-based Game Theory—0
Self-Route: Automatic Mode Switching via Capability Estimation for Efficient Reasoning—0
A Semantic Change Detection Network Based on Boundary Detection and Task Interaction for High-Resolution Remote Sensing ImagesCode1
Tropical Geometry Based Edge Detection Using Min-Plus and Max-Plus Algebra—0
Rethinking Boundary Detection in Deep Learning-Based Medical Image SegmentationCode2
Tiger200K: Manually Curated High Visual Quality Video Dataset from UGC Platform—0
EventVAD: Training-Free Event-Aware Video Anomaly Detection—0
Caption Anything in Video: Fine-grained Object-centric Captioning via Spatiotemporal Multimodal PromptingCode2
Foundations and Evaluations in NLP—0
Physics-informed neural networks for hidden boundary detection and flow field reconstruction—0
BoundMatch: Boundary detection applied to semi-supervised segmentation for urban-driving scenes—0
Open-World Skill Discovery from Unsegmented Demonstrations—0
TimeLoc: A Unified End-to-End Framework for Precise Timestamp Localization in Long VideosCode1
Road Boundary Detection Using 4D mmWave Radar for Autonomous Driving—0
JiTTER: Jigsaw Temporal Transformer for Event Reconstruction for Self-Supervised Sound Event DetectionCode0
Improving action segmentation via explicit similarity measurement—0
Agricultural Field Boundary Detection through Integration of "Simple Non-Iterative Clustering (SNIC) Super Pixels" and "Canny Edge Detection Method"Code0
Test-Time Code-Switching for Cross-lingual Aspect Sentiment Triplet Extraction—0
Unsupervised Speech Segmentation: A General Approach Using Speech Language ModelsCode1
Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation—0
LongVALE: Vision-Audio-Language-Event Benchmark Towards Time-Aware Omni-Modal Perception of Long VideosCode2
Back to Supervision: Boosting Word Boundary Detection through Frame ClassificationCode0
TriG-NER: Triplet-Grid Framework for Discontinuous Named Entity RecognitionCode0
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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