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Found 10 papers, 1 papers with code
Intra-operative Brain Tumor Detection with Deep Learning-Optimized Hyperspectral Imaging
Surgery for gliomas (intrinsic brain tumors), especially when low-grade, is challenging due to the infiltrative nature of the lesion.
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.
N2V2 -- Fixing Noise2Void Checkerboard Artifacts with Modified Sampling Strategies and a Tweaked Network Architecture
We validate our modifications on a range of microscopy and natural image data.
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.
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.
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.
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.
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.
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.
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.
