Proceedings ArticleDOI
Real-time Instance Detection with Fast Incremental Learning
Richard Bormann,Xinjie Wang,Markus Volk,Kilian Kleeberger,Jochen Lindermayr +4 more
- pp 13056-13063
TLDR
In this article, an ensemble of efficient single-class instance detectors capable of fast and incremental adaptation to new object sets is proposed, which can be obtained within less than 40 minutes on a consumer GPU while only a small percentage of the existing detection models need to be updated.Abstract:
Object instance detection is a highly relevant task to several robotic applications such as automated order picking, or household and hospital assistance robots. In these applications, a holistic scene labeling is often not required whereas it is sufficient to find a certain object type of interest, e.g. for picking it up. At the same time, large and continuously changing object sets are characteristic in such applications, requiring efficient model update capabilities from the object detector. Today’s monolithic multi-class detectors do not fulfill this criterion for fast and flexible model updates.This paper introduces InstanceNet, an ensemble of efficient single-class instance detectors capable of fast and incremental adaptation to new object sets. Due to a dynamic sampling-based training strategy, accurate detection models for new objects can be obtained within less than 40 minutes on a consumer GPU while only a small percentage of the existing detection models needs to be updated in a very efficient manner. The new detector has been thoroughly evaluated on the basis of a novel dataset of 100 grocery store objects.read more
Citations
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Proceedings ArticleDOI
Fast Hierarchical Learning for Few-Shot Object Detection
TL;DR: This work poses few-shot detection as a hierarchical learning problem, where the novel classes are treated as the child classes of existing base classes and the background class, leading to lower training times compared to stochastic gradient descent.
Journal ArticleDOI
Data-Driven Robotic Tactile Grasping for Hyper-Personalization Line Pick-and-Place
TL;DR: In this article , the Rochu two-fingered soft gripper with customized force-sensing resistor (FSR) force sensors mounted on the fingertips was used to improve the robotic grasping capability for novel objects.
Proceedings ArticleDOI
Fast Hierarchical Learning for Few-Shot Object Detection
TL;DR: In this paper , the authors pose few-shot detection as a hierarchical learning problem, where the novel classes are treated as the child classes of existing base classes and the background class.
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