Joachim Denzler

research

∙ 07/28/2023

Automated Visual Monitoring of Nocturnal Insects with Light-based Camera Traps

Automatic camera-assisted monitoring of insects for abundance estimation...

0 Dimitri Korsch, et al. ∙

research

∙ 07/28/2023

Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification

Biodiversity monitoring is crucial for tracking and counteracting advers...

0 Dimitri Korsch, et al. ∙

research

∙ 07/27/2023

Simplified Concrete Dropout – Improving the Generation of Attribution Masks for Fine-grained Classification

Fine-grained classification is a particular case of a classification pro...

0 Dimitri Korsch, et al. ∙

research

∙ 07/17/2023

Improving Data Efficiency for Plant Cover Prediction with Label Interpolation and Monte-Carlo Cropping

The plant community composition is an essential indicator of environment...

0 Matthias Körschens, et al. ∙

research

∙ 12/21/2022

Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series

Detecting anomalies in time series data is important in a variety of fie...

0 Ferdinand Rewicki, et al. ∙

research

∙ 09/23/2022

Sequential Causal Effect Variational Autoencoder: Time Series Causal Link Estimation under Hidden Confounding

Estimating causal effects from observational data in the presence of lat...

0 Violeta Teodora Trifunov, et al. ∙

research

∙ 07/08/2022

Causal Discovery using Model Invariance through Knockoff Interventions

Cause-effect analysis is crucial to understand the underlying mechanism ...

0 Wasim Ahmad, et al. ∙

research

∙ 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...

10 Bernd Gruner, et al. ∙

research

∙ 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...

0 Björn Barz, et al. ∙

research

∙ 09/22/2021

Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals

Estimating causal relations is vital in understanding the complex intera...

0 Wasim Ahmad, et al. ∙

research

∙ 09/14/2021

Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning

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

0 Violeta Teodora Trifunov, et al. ∙

research

∙ 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...

0 Lorenzo Brigato, et al. ∙

research

∙ 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...

0 Björn Barz, et al. ∙

research

∙ 06/21/2021

Automatic Plant Cover Estimation with CNNs Automatic Plant Cover Estimation with Convolutional Neural Networks

Monitoring the responses of plants to environmental changes is essential...

0 Matthias Körschens, et al. ∙

research

∙ 04/22/2021

Self-Supervised Learning from Semantically Imprecise Data

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

0 Clemens-Alexander Brust, et al. ∙

research

∙ 04/16/2021

EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task

Satellite images are snapshots of the Earth surface. We propose to forec...

0 Christian Requena-Mesa, et al. ∙

research

∙ 03/10/2021

Towards Learning an Unbiased Classifier from Biased Data via Conditional Adversarial Debiasing

Bias in classifiers is a severe issue of modern deep learning methods, e...

0 Christian Reimers, et al. ∙

research

∙ 02/01/2021

Counterfactual Generation with Knockoffs

Human interpretability of deep neural networks' decisions is crucial, es...

5 Oana-Iuliana Popescu, et al. ∙

research

∙ 12/16/2020

Analysing the Direction of Emotional Influence in Nonverbal Dyadic Communication: A Facial-Expression Study

Identifying the direction of emotional influence in a dyadic dialogue is...

0 Maha Shadaydeh, et al. ∙

research

∙ 12/11/2020

EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts

Climate change is global, yet its concrete impacts can strongly vary bet...

0 Christian Requena-Mesa, et al. ∙

research

∙ 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...

0 Björn Barz, et al. ∙

research

∙ 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...

0 Björn Barz, et al. ∙

research

∙ 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...

0 Clemens-Alexander Brust, et al. ∙

research

∙ 07/04/2020

End-to-end Learning of a Fisher Vector Encoding for Part Features in Fine-grained Recognition

Part-based approaches for fine-grained recognition do not show the expec...

0 Dimitri Korsch, et al. ∙

research

∙ 06/23/2020

Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometry Constrained Keypoints in Real-Time

In this paper we propose a novel 3D single-shot object detection method ...

25 Nils Gählert, et al. ∙

research

∙ 06/15/2020

Visibility Guided NMS: Efficient Boosting of Amodal Object Detection in Crowded Traffic Scenes

Object detection is an important task in environment perception for auto...

0 Nils Gählert, et al. ∙

research

∙ 06/14/2020

Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection

Detecting vehicles and representing their position and orientation in th...

0 Nils Gählert, et al. ∙

research

∙ 06/09/2020

Sensor Artificial Intelligence and its Application to Space Systems – A White Paper

Information and communication technologies have accompanied our everyday...

0 Anko Börner, et al. ∙

research

∙ 01/20/2020

Active and Incremental Learning with Weak Supervision

Large amounts of labeled training data are one of the main contributors ...

0 Clemens-Alexander Brust, et al. ∙

research

∙ 10/16/2019

LOST: A flexible framework for semi-automatic image annotation

State-of-the-art computer vision approaches rely on huge amounts of anno...

0 Jonas Jäger, et al. ∙

research

∙ 10/14/2019

Facial Behavior Analysis using 4D Curvature Statistics for Presentation Attack Detection

The uniqueness, complexity, and diversity of facial shapes and expressio...

66 Martin Thümmel, et al. ∙

research

∙ 09/23/2019

Predicting Landscapes from Environmental Conditions Using Generative Networks

Landscapes are meaningful ecological units that strongly depend on the e...

26 Christian Requena-Mesa, et al. ∙

research

∙ 09/16/2019

Classification-Specific Parts for Improving Fine-Grained Visual Categorization

Fine-grained visual categorization is a classification task for distingu...

0 Dimitri Korsch, et al. ∙

research

∙ 08/12/2019

Self-supervised Data Bootstrapping for Deep Optical Character Recognition of Identity Documents

The essential task of verifying person identities at airports and nation...

0 Oliver Mothes, et al. ∙

research

∙ 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...

0 Björn Barz, et al. ∙

research

∙ 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...

0 Björn Barz, et al. ∙

research

∙ 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...

0 Björn Barz, et al. ∙

research

∙ 12/11/2018

Towards Automatic Identification of Elephants in the Wild

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

0 Matthias Körschens, et al. ∙

research

∙ 11/17/2018

Integrating domain knowledge: using hierarchies to improve deep classifiers

One of the most prominent problems in machine learning in the age of dee...

0 Clemens-Alexander Brust, et al. ∙

research

∙ 11/17/2018

Not just a matter of semantics: the relationship between visual similarity and semantic similarity

Knowledge transfer, zero-shot learning and semantic image retrieval are ...

0 Clemens-Alexander Brust, et al. ∙

research

∙ 10/29/2018

Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality

Human nonverbal emotional communication in dyadic dialogs is a process o...

1 Lea Müller, et al. ∙

research

∙ 09/26/2018

Hierarchy-based Image Embeddings for Semantic Image Retrieval

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

2 Björn Barz, et al. ∙

research

∙ 09/26/2018

Active Learning for Deep Object Detection

The great success that deep models have achieved in the past is mainly o...

0 Clemens-Alexander Brust, et al. ∙

research

∙ 09/07/2018

Information-Theoretic Active Learning for Content-Based Image Retrieval

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

6 Björn Barz, et al. ∙

research

∙ 04/19/2018

Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection

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

0 Björn Barz, et al. ∙

research

∙ 11/02/2017

Automatic Query Image Disambiguation for Content-Based Image Retrieval

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

0 Björn Barz, et al. ∙

research

∙ 05/23/2017

Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues

Evaluating expression of the Human epidermal growth factor receptor 2 (H...

0 Talha Qaiser, et al. ∙

research

∙ 04/10/2017

Fast Learning and Prediction for Object Detection using Whitened CNN Features

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

0 Björn Barz, et al. ∙

research

∙ 03/05/2017

Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning

Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the or...

0 Marc Aubreville, et al. ∙

research

∙ 12/19/2016

Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes

The demands on visual recognition systems do not end with the complexity...

0 Christoph Käding, et al. ∙

Joachim Denzler

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