research
∙
12/08/2022
Evaluating Zero-cost Active Learning for Object Detection
Object detection requires substantial labeling effort for learning robus...
share
research
∙
04/27/2021
Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentation
Pixel-wise segmentation is one of the most data and annotation hungry ta...
research
∙
04/19/2018
Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection
Automatic detection of anomalies in space- and time-varying measurements...
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...
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...
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...
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...
research
∙
12/05/2016
ImageNet pre-trained models with batch normalization
Convolutional neural networks (CNN) pre-trained on ImageNet are the back...
research
∙
10/21/2016
Maximally Divergent Intervals for Anomaly Detection
We present new methods for batch anomaly detection in multivariate time ...
research
∙
10/21/2016
Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches
In this paper, we study the sensitivity of CNN outputs with respect to i...
research
∙
10/10/2016
Impatient DNNs - Deep Neural Networks with Dynamic Time Budgets
We propose Impatient Deep Neural Networks (DNNs) which deal with dynamic...
research
∙
06/14/2016
Neither Quick Nor Proper -- Evaluation of QuickProp for Learning Deep Neural Networks
Neural networks and especially convolutional neural networks are of grea...
research
∙
07/03/2015
Fine-grained Recognition Datasets for Biodiversity Analysis
In the following paper, we present and discuss challenging applications ...
research
∙
04/30/2015
Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks
Part models of object categories are essential for challenging recogniti...
research
∙
02/23/2015
Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding
Classifying single image patches is important in many different applicat...
research
∙
11/12/2014
Part Detector Discovery in Deep Convolutional Neural Networks
Current fine-grained classification approaches often rely on a robust lo...
research
∙
08/20/2014
Seeing through bag-of-visual-word glasses: towards understanding quantization effects in feature extraction methods
Vector-quantized local features frequently used in bag-of-visual-words a...
research
∙
07/10/2014
ARTOS -- Adaptive Real-Time Object Detection System
ARTOS is all about creating, tuning, and applying object detection model...
research
∙
10/17/2013
Fine-grained Categorization -- Short Summary of our Entry for the ImageNet Challenge 2012
In this paper, we tackle the problem of visual categorization of dog bre...
research
∙
08/20/2013
Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with Implicit Low-rank Transformations
Images seen during test time are often not from the same distribution as...
research
∙
11/06/2012