Search Results for author: Björn Barz

Found 22 papers, 9 papers with code

Data-Driven Detection of General Chiasmi Using Lexical and Semantic Features

no code implementations EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 Felix Schneider, Björn Barz, Phillip Brandes, Sophie Marshall, Joachim Denzler

In contrast, we propose an approach targeting the more general and challenging case A B B’ A’, where the words A, A’ and B, B’ constituting the chiasmus do not need to be identical but just related in meaning.

Argument Mining Word Embeddings

Image Classification With Small Datasets: Overview and Benchmark

1 code implementation IEEE Access 2022 Lorenzo Brigato, Björn Barz, Luca Iocchi, Joachim Denzler

However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common benchmark to allow for objective comparisons between published methods.

Classification Small Data Image Classification

Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity

no code implementations22 Oct 2021 Bernd Gruner, Matthias Körschens, Björn Barz, Joachim Denzler

We discovered that domain adaptation works very well for fine-grained recognition and that the normalization methods have a great influence on the results.

Active Learning Transfer Learning +1

A Strong Baseline for the VIPriors Data-Efficient Image Classification Challenge

no code implementations28 Sep 2021 Björn Barz, Lorenzo Brigato, Luca Iocchi, Joachim Denzler

Learning from limited amounts of data is the hallmark of intelligence, requiring strong generalization and abstraction skills.

Classification Image Classification

Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning

no code implementations14 Sep 2021 Violeta Teodora Trifunov, Maha Shadaydeh, Björn Barz, Joachim Denzler

There are numerous methods for detecting anomalies in time series, but that is only the first step to understanding them.

Time Series Analysis

Tune It or Don't Use It: Benchmarking Data-Efficient Image Classification

1 code implementation30 Aug 2021 Lorenzo Brigato, Björn Barz, Luca Iocchi, Joachim Denzler

Data-efficient image classification using deep neural networks in settings, where only small amounts of labeled data are available, has been an active research area in the recent past.

Benchmarking Small Data Image Classification

WikiChurches: A Fine-Grained Dataset of Architectural Styles with Real-World Challenges

no code implementations16 Aug 2021 Björn Barz, Joachim Denzler

We introduce a novel dataset for architectural style classification, consisting of 9, 485 images of church buildings.

Self-Supervised Learning from Semantically Imprecise Data

no code implementations22 Apr 2021 Clemens-Alexander Brust, Björn Barz, Joachim Denzler

Learning from imprecise labels such as "animal" or "bird", but making precise predictions like "snow bunting" at inference time is an important capability for any classifier when expertly labeled training data is scarce.

Self-Supervised Learning

Content-based Image Retrieval and the Semantic Gap in the Deep Learning Era

no code implementations12 Nov 2020 Björn Barz, Joachim Denzler

Content-based image retrieval has seen astonishing progress over the past decade, especially for the task of retrieving images of the same object that is depicted in the query image.

Content-Based Image Retrieval Retrieval

Finding Relevant Flood Images on Twitter using Content-based Filters

1 code implementation11 Nov 2020 Björn Barz, Kai Schröter, Ann-Christin Kra, Joachim Denzler

The analysis of natural disasters such as floods in a timely manner often suffers from limited data due to coarsely distributed sensors or sensor failures.

Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Images

1 code implementation9 Aug 2019 Björn Barz, Kai Schröter, Moritz Münch, Bin Yang, Andrea Unger, Doris Dransch, Joachim Denzler

The analysis of natural disasters such as floods in a timely manner often suffers from limited data due to a coarse distribution of sensors or sensor failures.

Content-Based Image Retrieval Retrieval

Do We Train on Test Data? Purging CIFAR of Near-Duplicates

no code implementations1 Feb 2019 Björn Barz, Joachim Denzler

However, we find that 3. 3% and 10% of the images from the test sets of these datasets have duplicates in the training set.

General Classification Memorization

Deep Learning on Small Datasets without Pre-Training using Cosine Loss

1 code implementation25 Jan 2019 Björn Barz, Joachim Denzler

The categorical cross-entropy loss after softmax activation is the method of choice for classification.

General Classification

Towards Automatic Identification of Elephants in the Wild

no code implementations11 Dec 2018 Matthias Körschens, Björn Barz, Joachim Denzler

Identifying animals from a large group of possible individuals is very important for biodiversity monitoring and especially for collecting data on a small number of particularly interesting individuals, as these have to be identified first before this can be done.

Hierarchy-based Image Embeddings for Semantic Image Retrieval

1 code implementation26 Sep 2018 Björn Barz, Joachim Denzler

Such an embedding does not only improve image retrieval results, but could also facilitate integrating semantics for other tasks, e. g., novelty detection or few-shot learning.

Few-Shot Learning Image Retrieval +3

Information-Theoretic Active Learning for Content-Based Image Retrieval

1 code implementation7 Sep 2018 Björn Barz, Christoph Käding, Joachim Denzler

We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode active learning method for binary classification, and apply it for acquiring meaningful user feedback in the context of content-based image retrieval.

Active Learning Content-Based Image Retrieval +1

Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection

1 code implementation19 Apr 2018 Björn Barz, Erik Rodner, Yanira Guanche Garcia, Joachim Denzler

Automatic detection of anomalies in space- and time-varying measurements is an important tool in several fields, e. g., fraud detection, climate analysis, or healthcare monitoring.

Anomaly Detection Fraud Detection +1

Automatic Query Image Disambiguation for Content-Based Image Retrieval

1 code implementation2 Nov 2017 Björn Barz, Joachim Denzler

Query images presented to content-based image retrieval systems often have various different interpretations, making it difficult to identify the search objective pursued by the user.

Content-Based Image Retrieval Retrieval

Fast Learning and Prediction for Object Detection using Whitened CNN Features

no code implementations10 Apr 2017 Björn Barz, Erik Rodner, Christoph Käding, Joachim Denzler

We combine features extracted from pre-trained convolutional neural networks (CNNs) with the fast, linear Exemplar-LDA classifier to get the advantages of both: the high detection performance of CNNs, automatic feature engineering, fast model learning from few training samples and efficient sliding-window detection.

Feature Engineering object-detection +1

ARTOS -- Adaptive Real-Time Object Detection System

no code implementations10 Jul 2014 Björn Barz, Erik Rodner, Joachim Denzler

ARTOS is all about creating, tuning, and applying object detection models with just a few clicks.

object-detection Real-Time Object Detection

Cannot find the paper you are looking for? You can Submit a new open access paper.