Search Results for author:
Found 30 papers, 6 papers with code
Efficient Learning of Domain-invariant Image Representations
We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers.
Semi-supervised Domain Adaptation with Instance Constraints
Most successful object classification and detection methods rely on classifiers trained on large labeled datasets.
Kernel Null Space Methods for Novelty Detection
In contrast to modeling the support of each known class individually, our approach makes use of a projection in a joint subspace where training samples of all known classes have zero intra-class variance.
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 images used for learning.
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 breeds, which is a surprisingly challenging task due to simultaneously present low interclass distances and high intra-class variances.
Instance-weighted Transfer Learning of Active Appearance Models
Therefore, we present a transfer learning method that is able to learn from related training data using an instance-weighted transfer technique.
Nonparametric Part Transfer for Fine-grained Recognition
In the following paper, we present an approach for finegrained recognition based on a new part detection method.
ARTOS -- Adaptive Real-Time Object Detection System
ARTOS is all about creating, tuning, and applying object detection models with just a few clicks.
Seeing through bag-of-visual-word glasses: towards understanding quantization effects in feature extraction methods
To answer this question, we present an in-depth analysis of the effect of local feature quantization on human recognition performance.
Part Detector Discovery in Deep Convolutional Neural Networks
Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination.
Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding
Classifying single image patches is important in many different applications, such as road detection or scene understanding.
Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks
Part models of object categories are essential for challenging recognition tasks, where differences in categories are subtle and only reflected in appearances of small parts of the object.
Active Learning and Discovery of Object Categories in the Presence of Unnameable Instances
In active learning, all categories occurring in collected data are usually assumed to be known in advance and experts should be able to label every requested instance.
Fine-grained Recognition Datasets for Biodiversity Analysis
In the following paper, we present and discuss challenging applications for fine-grained visual classification (FGVC): biodiversity and species analysis.
Neither Quick Nor Proper -- Evaluation of QuickProp for Learning Deep Neural Networks
Neural networks and especially convolutional neural networks are of great interest in current computer vision research.
Impatient DNNs - Deep Neural Networks with Dynamic Time Budgets
We propose Impatient Deep Neural Networks (DNNs) which deal with dynamic time budgets during application.
Maximally Divergent Intervals for Anomaly Detection
We present new methods for batch anomaly detection in multivariate time series.
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 image transformations and noise in the area of fine-grained recognition.
ImageNet pre-trained models with batch normalization
Convolutional neural networks (CNN) pre-trained on ImageNet are the backbone of most state-of-the-art approaches.
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 offered by current large-scale image datasets, such as ImageNet.
Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning
For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion.
Fast Learning and Prediction for Object Detection using Whitened CNN Features
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.
Generalized orderless pooling performs implicit salient matching
In this paper, we generalize average and bilinear pooling to "alpha-pooling", allowing for learning the pooling strategy during training.
Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues
In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring.
Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection
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.
Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentation
Pixel-wise segmentation is one of the most data and annotation hungry tasks in our field.
Evaluating Zero-cost Active Learning for Object Detection
Object detection requires substantial labeling effort for learning robust models.
Decoupled Semantic Prototypes Enable Learning From Diverse Annotation Types for Semi-Weakly Segmentation in Expert-Driven Domains
A vast amount of images and pixel-wise annotations allowed our community to build scalable segmentation solutions for natural domains.
CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object Classification
In this paper, we systematically analyze unsupervised domain adaptation pipelines for object classification in a challenging industrial setting.
Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective
A key challenge in machine learning is to design interpretable models that can reduce their inputs to the best subset for making transparent predictions, especially in the clinical domain.
