The Multimodal Annotation Software Tool (MAST)
Multimodal combinations of writing and pictures have become ubiquitous in contemporary society, and scholars have increasingly been turning to analyzing these media. Here we present a resource for annotating these complex documents: the Multimodal Annotation Software Tool (MAST). MAST is an application that allows users to analyze visual and multimodal documents by selecting and annotating visual regions, and to establish relations between annotations that create dependencies and/or constituent structures. By means of schema publications, MAST allows annotation theories to be citable, while evolving and being shared. Documents can be annotated using multiple schemas simultaneously, offering more comprehensive perspectives. As a distributed, client-server system MAST allows for collaborative annotations across teams of users, and features team management and resource access functionalities, facilitating the potential for implementing open science practices. Altogether, we aim for MAST to provide a powerful and innovative annotation tool with application across numerous fields engaging with multimodal media.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementSimilar Papers 제목 키워드 기반
Open community platform for hearing aid algorithm research: open Master Hearing Aid (openMHA)
open Master Hearing Aid (openMHA) was developed and provided to the hearing aid research community as an open-source software platform with the aim to support sustainable and reproducible research towards improvement and…
CellMaster: Collaborative Cell Type Annotation in Single-Cell Analysis
Single-cell RNA-seq (scRNA-seq) enables atlas-scale profiling of complex tissues, revealing rare lineages and transient states. Yet, assigning biologically valid cell identities remains a bottleneck because markers are t…
Deploy-Master: Automating the Deployment of 50,000+ Agent-Ready Scientific Tools in One Day
Open-source scientific software is abundant, yet most tools remain difficult to compile, configure, and reuse, sustaining a small-workshop mode of scientific computing. This deployment bottleneck limits reproducibility, …
MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition
Multimodal emotion recognition is an important research topic in artificial intelligence. Over the past few decades, researchers have made remarkable progress by increasing the dataset size and building more effective al…
Emotion RecognitionMulti-Label LearningMultimodal Emotion RecognitionSWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training
In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents. SWE-Master systematically explores the complete agent dev…