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
Libraries
Use these libraries to find Boundary Detection models and implementationsDatasets
Latest papers
TextMachina: Seamless Generation of Machine-Generated Text Datasets
Recent advancements in Large Language Models (LLMs) have led to high-quality Machine-Generated Text (MGT), giving rise to countless new use cases and applications.
Using Curiosity for an Even Representation of Tasks in Continual Offline Reinforcement Learning
In this work, we investigate the means of using curiosity on replay buffers to improve offline multi-task continual reinforcement learning when tasks, which are defined by the non-stationarity in the environment, are non labeled and not evenly exposed to the learner in time.
Mobile-Seed: Joint Semantic Segmentation and Boundary Detection for Mobile Robots
Our framework features a two-stream encoder, an active fusion decoder (AFD) and a dual-task regularization approach.
TSP-Transformer: Task-Specific Prompts Boosted Transformer for Holistic Scene Understanding
Holistic scene understanding includes semantic segmentation, surface normal estimation, object boundary detection, depth estimation, etc.
Antarlekhaka: A Comprehensive Tool for Multi-task Natural Language Annotation
In this paper, we present Antarlekhaka, a tool for manual annotation of a comprehensive set of tasks relevant to NLP.
Local Compressed Video Stream Learning for Generic Event Boundary Detection
Specifically, we use lightweight ConvNets to extract features of the P-frames in the GOPs and spatial-channel attention module (SCAM) is designed to refine the feature representations of the P-frames based on the compressed information with bidirectional information flow.
OTAS: Unsupervised Boundary Detection for Object-Centric Temporal Action Segmentation
In this paper, we explore the merits of local features by proposing the unsupervised framework of Object-centric Temporal Action Segmentation (OTAS).
Self-Similarity-Based and Novelty-based loss for music structure analysis
Music Structure Analysis (MSA) is the task aiming at identifying musical segments that compose a music track and possibly label them based on their similarity.
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.
A Unified Query-based Paradigm for Camouflaged Instance Segmentation
Due to the high similarity between camouflaged instances and the background, the recently proposed camouflaged instance segmentation (CIS) faces challenges in accurate localization and instance segmentation.