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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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 21–30 of 359 papers

Title	Date	Tasks	Status	Hype
Advancing Hungarian Text Processing with HuSpaCy: Efficient and Accurate NLP Pipelines	Aug 24, 2023	AllBoundary Detection	CodeCode Available	1
Prompt Guided Transformer for Multi-Task Dense Prediction	Jul 28, 2023	Boundary DetectionDecoder	CodeCode Available	1
MAE-GEBD:Winning the CVPR'2023 LOVEU-GEBD Challenge	Jun 27, 2023	Boundary DetectionGeneric Event Boundary Detection	CodeCode Available	1
Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation	May 1, 2023	Boundary DetectionDecoder	CodeCode Available	1
Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections	Dec 6, 2022	Boundary DetectionInstance Segmentation	CodeCode Available	1
Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module	Nov 17, 2022	Boundary DetectionSegmentation	CodeCode Available	1
HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images	Sep 1, 2022	Boundary DetectionDecoder	CodeCode Available	1
Masked Autoencoders for Generic Event Boundary Detection CVPR'2022 Kinetics-GEBD Challenge	Jun 17, 2022	Boundary DetectionGeneric Event Boundary Detection	CodeCode Available	1
Federated Learning Enables Big Data for Rare Cancer Boundary Detection	Apr 22, 2022	Boundary DetectionFederated Learning	CodeCode Available	1
Transformer and CNN Hybrid Deep Neural Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery	Jan 19, 2022	Boundary DetectionDecoder	CodeCode Available	1

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All datasets RoFT RoFT-chatgpt CoAuthor NYU-Depth V2 TriBERT (in-domain)UruDendro Kinetics 400 PASCAL Context

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	GigaCheck (DN-DAB-DETR)	Accuracy (%)	64.63	—	Unverified
2	RoBERTa + SEP	Accuracy (%)	49.64	—	Unverified
3	PHD + TS ML	Accuracy (%)	23.5	—	Unverified
4	TLE + TS Binary	Accuracy (%)	12.58	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GigaCheck (DN-DAB-DETR)	Accuracy (%)	67.65	—	Unverified
2	RoBERTa + SEP	Accuracy (%)	54.61	—	Unverified
3	TLE + TS Binary	Accuracy (%)	20.02	—	Unverified
4	PHD + TS ML	Accuracy (%)	17.29	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GigaCheck (Mistral-7B-v0.3)	Cohen’s Kappa score	0.42	—	Unverified
2	DeBERTa-v3 (Naive)	Cohen’s Kappa score	0.4	—	Unverified
3	GigaCheck (DN-DAB-DETR)	Cohen’s Kappa score	0.19	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	InvPT	odsF	78.1	—	Unverified
2	PGT (Swin-S)	odsF	78.04	—	Unverified
3	PGT (Swin-T)	odsF	77.05	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GigaCheck (DN-DAB-DETR)	F1@3	0.65	—	Unverified
2	TriBERT (p=2)	F1@3	0.58	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CS-TRD	Average Precision	0.94	—	Unverified
2	INBD	Average Precision	0.75	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CASTANET+ Ensemble	Pairwise F1	0.81	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	InvPT	odsF	73	—	Unverified