Boundary Detection
99 papers with code • 3 benchmarks • 10 datasets
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
Benchmarks
These leaderboards are used to track progress in Boundary Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
Libraries
Use these libraries to find Boundary Detection models and implementationsDatasets
Kinetics
NYUv2
Kinetics 400
PASCAL Context
SoccerNet-v2
UruDendro
CaFFe
Oxford Road Boundaries
Most implemented papers
An Underwater Image Semantic Segmentation Method Focusing on Boundaries and a Real Underwater Scene Semantic Segmentation Dataset
Based on this dataset, we propose a semi-supervised underwater semantic segmentation network focusing on the boundaries(US-Net: Underwater Segmentation Network).
Towards Automatic Boundary Detection for Human-AI Collaborative Hybrid Essay in Education
douglashiwo/boundarydetection
•
•
Then we proposed a two-step approach where we (1) separated AI-generated content from human-written content during the encoder training process; and (2) calculated the distances between every two adjacent prototypes and assumed that the boundaries exist between the two adjacent prototypes that have the furthest distance from each other.
Advancing Hungarian Text Processing with HuSpaCy: Efficient and Accurate NLP Pipelines
This paper presents a set of industrial-grade text processing models for Hungarian that achieve near state-of-the-art performance while balancing resource efficiency and accuracy.
Dense Volume-to-Volume Vascular Boundary Detection
In this work, we present a novel 3D-Convolutional Neural Network (CNN) architecture called I2I-3D that predicts boundary location in volumetric data.
UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory
In this work we introduce a convolutional neural network (CNN) that jointly handles low-, mid-, and high-level vision tasks in a unified architecture that is trained end-to-end.
Named Entity Recognition in Swedish Health Records with Character-Based Deep Bidirectional LSTMs
We propose an approach for named entity recognition in medical data, using a character-based deep bidirectional recurrent neural network.
Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection
deep-unlearn/ISPRS-Classification-With-an-Edge
•
•
We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries.
Entity Identification as Multitasking
Standard approaches in entity identification hard-code boundary detection and type prediction into labels (e. g., John/B-PER Smith/I-PER) and then perform Viterbi.
Joint RNN Model for Argument Component Boundary Detection
nicoManthey/Mining-Claims-in-UNHCR-reports-A2
•
•
Argument Component Boundary Detection (ACBD) is an important sub-task in argumentation mining; it aims at identifying the word sequences that constitute argument components, and is usually considered as the first sub-task in the argumentation mining pipeline.
The Devil is in the Decoder: Classification, Regression and GANs
Many machine vision applications, such as semantic segmentation and depth prediction, require predictions for every pixel of the input image.
