24,306 research outputs found
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Subset Feature Learning for Fine-Grained Category Classification
Fine-grained categorisation has been a challenging problem due to small
inter-class variation, large intra-class variation and low number of training
images. We propose a learning system which first clusters visually similar
classes and then learns deep convolutional neural network features specific to
each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset
show that the proposed method outperforms recent fine-grained categorisation
methods under the most difficult setting: no bounding boxes are presented at
test time. It achieves a mean accuracy of 77.5%, compared to the previous best
performance of 73.2%. We also show that progressive transfer learning allows us
to first learn domain-generic features (for bird classification) which can then
be adapted to specific set of bird classes, yielding improvements in accuracy
Image Representations and New Domains in Neural Image Captioning
We examine the possibility that recent promising results in automatic caption
generation are due primarily to language models. By varying image
representation quality produced by a convolutional neural network, we find that
a state-of-the-art neural captioning algorithm is able to produce quality
captions even when provided with surprisingly poor image representations. We
replicate this result in a new, fine-grained, transfer learned captioning
domain, consisting of 66K recipe image/title pairs. We also provide some
experiments regarding the appropriateness of datasets for automatic captioning,
and find that having multiple captions per image is beneficial, but not an
absolute requirement.Comment: 11 Pages, 5 Images, To appear at EMNLP 2015's Vision + Learning
worksho
- …