Björn Barz | DeepAI

DeepAI

Featured Co-authors

Bin Yang
128 publications
Joachim Denzler
58 publications
Erik Rodner
21 publications
Luca Iocchi
11 publications
Clemens-Alexander Brust
11 publications
Markus Reichstein
9 publications
Maha Shadaydeh
8 publications
Paul Bodesheim
6 publications
Christoph Käding
5 publications
Bernd Gruner
4 publications
Matthias Körschens
3 publications

∙ 10/22/2021

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

Deep-learning methods offer unsurpassed recognition performance in a wid...

Bernd Gruner, et al. ∙

∙ 09/28/2021

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

Learning from limited amounts of data is the hallmark of intelligence, r...

Björn Barz, et al. ∙

∙ 09/14/2021

Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning

There are numerous methods for detecting anomalies in time series, but t...

Violeta Teodora Trifunov, et al. ∙

∙ 08/30/2021

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

Data-efficient image classification using deep neural networks in settin...

Lorenzo Brigato, et al. ∙

∙ 08/16/2021

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

We introduce a novel dataset for architectural style classification, con...

Björn Barz, et al. ∙

∙ 04/22/2021

Self-Supervised Learning from Semantically Imprecise Data

Learning from imprecise labels such as "animal" or "bird", but making pr...

Clemens-Alexander Brust, et al. ∙

∙ 11/12/2020

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

Content-based image retrieval has seen astonishing progress over the pas...

Björn Barz, et al. ∙

∙ 11/11/2020

Finding Relevant Flood Images on Twitter using Content-based Filters

The analysis of natural disasters such as floods in a timely manner ofte...

Björn Barz, et al. ∙

∙ 10/13/2020

Making Every Label Count: Handling Semantic Imprecision by Integrating Domain Knowledge

Noisy data, crawled from the web or supplied by volunteers such as Mecha...

Clemens-Alexander Brust, et al. ∙

∙ 08/09/2019

Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Images

The analysis of natural disasters such as floods in a timely manner ofte...

Björn Barz, et al. ∙

∙ 02/01/2019

Do we train on test data? Purging CIFAR of near-duplicates

We find that 3.3 sets, respectively, have duplicates in the training set...

Björn Barz, et al. ∙

∙ 01/25/2019

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

Two things seem to be indisputable in the contemporary deep learning dis...

Björn Barz, et al. ∙

∙ 12/11/2018

Towards Automatic Identification of Elephants in the Wild

Identifying animals from a large group of possible individuals is very i...

Matthias Körschens, et al. ∙

∙ 09/26/2018

Hierarchy-based Image Embeddings for Semantic Image Retrieval

Deep neural networks trained for classification have been found to learn...

Björn Barz, et al. ∙

∙ 09/07/2018

Information-Theoretic Active Learning for Content-Based Image Retrieval

We propose Information-Theoretic Active Learning (ITAL), a novel batch-m...

Björn Barz, et al. ∙

∙ 04/19/2018

Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection

Automatic detection of anomalies in space- and time-varying measurements...

Björn Barz, et al. ∙

∙ 11/02/2017

Automatic Query Image Disambiguation for Content-Based Image Retrieval

Query images presented to content-based image retrieval systems often ha...

Björn Barz, et al. ∙

∙ 04/10/2017

Fast Learning and Prediction for Object Detection using Whitened CNN Features

We combine features extracted from pre-trained convolutional neural netw...

Björn Barz, et al. ∙

∙ 10/21/2016

Maximally Divergent Intervals for Anomaly Detection

We present new methods for batch anomaly detection in multivariate time ...

Erik Rodner, et al. ∙

∙ 07/10/2014

ARTOS -- Adaptive Real-Time Object Detection System

ARTOS is all about creating, tuning, and applying object detection model...

Björn Barz, et al. ∙