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
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Libraries
Use these libraries to find Boundary Detection models and implementationsDatasets
Kinetics
NYUv2
Kinetics 400
PASCAL Context
SoccerNet-v2
UruDendro
CaFFe
Oxford Road Boundaries
Latest papers with no code
No-frills Temporal Video Grounding: Multi-Scale Neighboring Attention and Zoom-in Boundary Detection
Temporal video grounding (TVG) aims to retrieve the time interval of a language query from an untrimmed video.
Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy
We combine scanning electron microscopy (SEM) images of 347H stainless steel as training data and electron backscatter diffraction (EBSD) micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task.
An unsupervised segmentation of vocal breath sounds
Dynamic programming with the prior information of the number of breath phases($P$) and breath phase duration($d$) is used to find the boundaries.
Multi-task Transformer with Relation-attention and Type-attention for Named Entity Recognition
There are three types of NER tasks, including flat, nested and discontinuous entity recognition.
B-BACN: Bayesian Boundary-Aware Convolutional Network for Crack Characterization
Accurately detecting crack boundaries is crucial for reliability assessment and risk management of structures and materials, such as structural health monitoring, diagnostics, prognostics, and maintenance scheduling.
Algorithm Design for Online Meta-Learning with Task Boundary Detection
More specifically, we first propose two simple but effective detection mechanisms of task switches and distribution shift based on empirical observations, which serve as a key building block for more elegant online model updates in our algorithm: the task switch detection mechanism allows reusing of the best model available for the current task at hand, and the distribution shift detection mechanism differentiates the meta model update in order to preserve the knowledge for in-distribution tasks and quickly learn the new knowledge for out-of-distribution tasks.
DeCo: Decomposition and Reconstruction for Compositional Temporal Grounding via Coarse-To-Fine Contrastive Ranking
Compositional temporal grounding is the task of localizing dense action by using known words combined in novel ways in the form of novel query sentences for the actual grounding.
Multimodal High-order Relation Transformer for Scene Boundary Detection
Scene boundary detection breaks down long videos into meaningful story-telling units and plays a crucial role in high-level video understanding.
Multi-Task Learning with Knowledge Distillation for Dense Prediction
With the less sensitive divergence, our knowledge distillation with an alternative match is applied for capturing inter-task and intra-task information between the teacher model and the student model of each task, thereby learning more "dark knowledge" for effective distillation.
Learning to Detect Semantic Boundaries with Image-level Class Labels
This paper presents the first attempt to learn semantic boundary detection using image-level class labels as supervision.
