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Alina Kuznetsova
Researcher at Google
Publications - 22
Citations - 1493
Alina Kuznetsova is an academic researcher from Google. The author has contributed to research in topics: Object detection & Object (computer science). The author has an hindex of 10, co-authored 20 publications receiving 1366 citations. Previous affiliations of Alina Kuznetsova include Kaiserslautern University of Technology & Leibniz University of Hanover.
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Journal ArticleDOI
The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale
Alina Kuznetsova,Hassan Rom,Neil Alldrin,Jasper Uijlings,Ivan Krasin,Jordi Pont-Tuset,Shahab Kamali,Stefan Popov,Matteo Malloci,Alexander Kolesnikov,Tom Duerig,Vittorio Ferrari +11 more
TL;DR: Open Images V4 as mentioned in this paper is a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection from Flickr without a predefined list of class names or tags.
Journal ArticleDOI
The Open Images Dataset V4: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale
Alina Kuznetsova,Hassan Rom,Neil Alldrin,Jasper Uijlings,Ivan Krasin,Jordi Pont-Tuset,Shahab Kamali,Stefan Popov,Matteo Malloci,Alexander Kolesnikov,Tom Duerig,Vittorio Ferrari +11 more
TL;DR: Open Images V4 as discussed by the authors is a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection from Flickr without a predefined list of class names or tags.
Proceedings ArticleDOI
Learning an Image-Based Motion Context for Multiple People Tracking
TL;DR: A novel method for multiple people tracking that leverages a generalized model for capturing interactions among individuals which is able to encode the effect of undetected targets, making the tracker more robust to partial occlusions.
Proceedings ArticleDOI
Real-Time Sign Language Recognition Using a Consumer Depth Camera
TL;DR: This work proposes a highly precise method to recognize static gestures from a depth data, provided from one of the above mentioned devices, using a multi-layered random forest (MLRF).
Proceedings ArticleDOI
Detecting Visual Relationships Using Box Attention
TL;DR: In this article, a box attention mechanism is proposed to model pairwise interactions between objects using standard object detection pipelines, and the resulting model is conceptually clean, expressive and relies on well-justified training and prediction procedures.