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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 271–280 of 359 papers

Title	Date	Tasks	Status	Hype
Combining Global Models for Parsing Universal Dependencies	Aug 1, 2017	Boundary DetectionDependency Parsing	—Unverified	0
A Locally Adapting Technique for Boundary Detection using Image Segmentation	Jul 27, 2017	Boundary DetectionImage Segmentation	—Unverified	0
The Devil is in the Decoder: Classification, Regression and GANs	Jul 18, 2017	Boundary DetectionDecoder	CodeCode Available	0
Ubernet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory	Jul 1, 2017	Boundary DetectionGPU	—Unverified	0
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks	May 23, 2017	Action RecognitionBoundary Detection	CodeCode Available	0
Joint RNN Model for Argument Component Boundary Detection	May 5, 2017	Boundary DetectionFeature Engineering	CodeCode Available	0
Improving Optical Character Recognition of Finnish Historical Newspapers with a Combination of Fraktur \& Antiqua Models and Image Preprocessing	May 1, 2017	Boundary DetectionInformation Retrieval	—Unverified	0
Entity Extraction in Biomedical Corpora: An Approach to Evaluate Word Embedding Features with PSO based Feature Selection	Apr 1, 2017	Boundary DetectionEntity Extraction using GAN	—Unverified	0
Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection	Mar 24, 2017	Boundary DetectionSegmentation	—Unverified	0
Skin cancer reorganization and classification with deep neural network	Mar 1, 2017	Boundary DetectionClassification	—Unverified	0

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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