In the past few years, object detection has attracted a lot of attention in the context of human–robot collaboration and Industry 5.0 due to enormous quality improvements in deep learning technologies. In many applications, object detection models have to be able to quickly adapt to a changing environment, i.e., to learn new objects. A crucial but challenging prerequisite for this is the automatic generation of new training data which currently still limits the broad application of object detection methods in industrial manufacturing. In this work, we discuss how to adapt state-of-the-art object detection methods for the task of automatic bounding box annotation in a use case where the background is homogeneous and the object’s label is provided by a human. We compare an adapted version of Faster R-CNN and the Scaled-YOLOv4-p5 architecture and show that both can be trained to distinguish unknown objects from a complex but homogeneous background using only a small amount of training data. In contrast to most other state-of-the-art methods for bounding box labeling, our proposed method neither requires human verification, a predefined set of classes, nor a very large manually annotated dataset. Our method outperforms the state-of-the-art, transformer-based object discovery method LOST on our simple fruits dataset by large margins.
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Shape Collage is a photo collage maker software program. Automatically create picture collages in a variety of shapes with just a few mouse clicks. Available for Windows, Mac OS X, and Linux.
java-emotion-recognizer - Java emotion recognition engine. Given training data and an input image, JEmotionRec can guess the emotion being conveyed with reasonable accuracy.
MSAGL is a .NET tool for graph layout and viewing. It was developed in Microsoft Research by Lev Nachmanson. MSAGL is built on the principle of the Sugiyama scheme; it produces so called layered, or hierarchical layouts. This kind of a layout naturally applies to graphs with some flow of information. The graph could represent a control flow graph of a program, a state machine, a C++ class hierarchy, etc.
AQUA - Automatic Quality Assessment and Feedback in eLearning 2.0
The current development of Web 2.0 makes the distinction between author and reader fading away. Users now produce huge amounts of data which sometimes is of questionable quality. This leads to the problem of information overload: how to make the most of this information without overwhelming the users? One key challenge to solve this issue is to assess the quality of the user generated content.
In AQUA, we seek to develop algorithms to assess the quality of content automatically. We focus on two sources for this assessment: (1) user generated content; (2) feedback by users of the content. To do so, we investigate techniques from the fields of natural language processing (NLP), information retrieval, and machine learning.
So, in a nutshell, AQUA will answer the following questions:
What is quality of information? How does it matter in information search?
How to model the quality of user generated content?
How far can you go with automatic methods in assessing quality?
How to give feedback to users regarding quality?
The AQUA project is associated with the project "Mining Lexical-Semantic Knowledge from Dynamic and Linguistic Sources and Integration into Question Answering for Discourse-Based Knowledge Acquisition in e-learning (QA-EL)".
An ontology is a computer-processable collection of knowledge about the world.
This thesis explains how an ontology can be constructed and expanded auto-
matically. The proposed approach consists of three contributions:
1. A core ontology, YAGO.
YAGO is an ontology that has been constructed automatically. It com-
bines high accuracy with large coverage and serves as a core that can be
expanded.
2. A tool for information extraction, LEILA.
LEILA is a system that can extract knowledge from natural language
texts. LEILA will be used to ¯nd new facts for YAGO.
3. An integration mechanism, SOFIE.
SOFIE is a system that can reason on the plausibility of new knowl-
edge. SOFIE will assess the facts found by LEILA and integrate them
into YAGO.
Each of these components comes with a fully implemented system. Together,
they form an integrative architecture, which does not only gather new facts,
but also reconcile them with the existing facts. The result is an ever-growing,
yet highly accurate ontological knowledge base. A survey of applications of the
ontology completes the thesis.
We present a taxonomy automatically generated from
the system of categories in Wikipedia. Categories in the resource
are identified as either classes or instances and included in a large
subsumption, i.e. isa, hierarchy. The taxonomy is made available in
RDFS format to the research community, e.g. for direct use within AI
applications or to bootstrap the process of manual ontology creation.
this paper presents the process of acquiring a large, domain independent, taxonomy from the German Wikipedia. We build upon a
previously implemented platform that extracts a semantic network and taxonomy from the English version of theWikipedia. We describe
two accomplishments of our work: the semantic network for the German language in which isa links are identied and annotated, and
an expansion of the platform for easy adaptation for a new language. We identify the platform's strengths and shortcomings, which stem
from the scarcity of free processing resources for languages other than English. We show that the taxonomy induction process is highly
reliable evaluated against the German version of WordNet, GermaNet, the resource obtained shows an accuracy of 83.34%.
This paper presents an automatic method for diferentiating
between instances and classes in a large scale taxonomy induced from
the Wikipedia category network. The method exploits characteristics
of the category names and the structure of the network. The approach
we present is the ¯rst attempt to make this distinction automatically
in a large scale resource. In contrast, this distinction has been made
in WordNet and Cyc based on manual annotations. The result of the
process is evaluated against ResearchCyc. On the subnetwork shared by
our taxonomy and ResearchCyc we report 84.52% accuracy.
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