Object Discovery and Object Proposal Generation
Object discovery is the task of finding unknown objects in a scene. Humans can easily distinguish objects from background without apparent effort, however, it is a challenging problem in machine vision with a chicken and egg problematic: how to tell what the objects are if their properties and features are not known? We propose approaches, inspired by the human visual system, to find object candidates in images and videos. |
Precise Proposals with CNN-based Superpixels (IMAVIS 2021):DeepFH Segmentations for Superpixel-based Object Proposal Refinement Christian Wilms, Simone Frintrop | |
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Object Proposals for Airline Logo Detection (ICPR 2020):Which Airline is This? Airline Logo Detection in Real-World Weather Conditions Christian Wilms, Rafael Heid, Mohammad Araf Sadeghi, Andreas Ribbrock, Simone Frintrop | |
Superpixels for Precise Object Proposals (ICPR 2020):Superpixel-based Refinement for Object Proposal Generation Christian Wilms, Simone Frintrop [Webpage] | |
Attention-based Object Proposals (ACCV 2018):AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects Christian Wilms, Simone Frintrop [Webpage] | |
Graph-based Object Discovery in RGB-D Data
Saliency-Guided Object Candidates Based on Gestalt Principles | |
Object discovery & tracking in videos (ICRA 2015,
Sequence-level Object Candidates Based on Saliency for Generic Object Recognition on Mobile Systems | |
Object discovery in RGB-D frames (ICRA 2015):Saliency-based Object Discovery on RGB-D Data with a Late-Fusion ApproachGermán Martín García, Ekaterina Potapova, Thomas Werner, Michael Zillich, Markus Vincze, and Simone Frintrop Datasets and results | |
Object discovery in images (ICPR 2014):A Cognitive Approach for Object DiscoverySimone Frintrop, Germán Martín García, and Armin B. Cremers | |
Object discovery and scene exploration in 3D (CogSci 2013):A Computational Framework for Attentional 3D Object DetectionGermán Martín García and Simone Frintrop |