The VIA Annotation Software for Images, Audio and Video

By Abhishek Dutta, Andrew Zisserman. 2019

1,056 citations recorded by the source corpus.

In this paper, we introduce a simple and standalone manual annotation tool for images, audio and video: the VGG Image Annotator (VIA). This is a light weight, standalone and offline software package that does not require any installation or setup and runs solely in a web browser. The VIA software allows human annotators to define and describe spatial regions in images or video frames, and temporal segments in audio or video. These manual annotations can be exported to plain text data formats such as JSON and CSV and therefore are amenable to further processing by other software tools. VIA also supports collaborative annotation of a large dataset by a group of human annotators. The BSD open source license of this software allows it to be used in any academic project or commercial application.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit (2021, background): “VGG Image Annotation Tool (VIA) [73] Bounding boxes and polygons COCO and specific CSV and JSON”
BABEL: Bodies, Action and Behavior with English Labels (2021, positive): “We leverage an existing video annotation tool, VIA [6], and modify the frontend interface and back-end functionality to suit our anno-”
The MECCANO Dataset: Understanding Human-Object Interactions from Egocentric Videos in an Industrial-like Domain (2020, methodology): “To this aim, we used VGG Image Annotator (VIA) [18] with a customized project which allowed annotators to select component classes from a dedicated panel showing the thumbnails of each of the 20 object classes to facilitate and speed up the selection of the correct object class.”
System Design and Control of an Apple Harvesting Robot (2020, positive): “A total of 1500 images were collected, and all apples in the images were annotated by rectangles using VGG Image Annotator [26].”
H2O: Two Hands Manipulating Objects for First Person Interaction Recognition (2021, methodology): “We select action labels for the entire dataset manually, using the VIA annotation tool [15].”
Self-Supervised Learning of Audio-Visual Objects from Video (2020, methodology): “For testing, we collected ASD and speaker localization labels, using the VIA tool [18]: we asked human annotators to label frames that they believed to contain an active speaker and to localize them.”
MECCANO: A Multimodal Egocentric Dataset for Humans Behavior Understanding in the Industrial-like Domain (2022, methodology): “For the annotation phase of the nextactive objects, we used VGG Image Annotator (VIA) [20] with a customized project to facilitate and speed up the selection of the correct object class (see Figure 23.”
Spot the conversation: speaker diarisation in the wild (2020, methodology): “The output of the automatic pipeline has been checked and corrected by the authors of this paper using a customised version of the VGG Image Annotator [35, 36].”