42,068 research outputs found
Learning Semantic Part-Based Models from Google Images
We propose a technique to train semantic part-based models of object classes
from Google Images. Our models encompass the appearance of parts and their
spatial arrangement on the object, specific to each viewpoint. We learn these
rich models by collecting training instances for both parts and objects, and
automatically connecting the two levels. Our framework works incrementally, by
learning from easy examples first, and then gradually adapting to harder ones.
A key benefit of this approach is that it requires no manual part location
annotations. We evaluate our models on the challenging PASCAL-Part dataset [1]
and show how their performance increases at every step of the learning, with
the final models more than doubling the performance of directly training from
images retrieved by querying for part names (from 12.9 to 27.2 AP). Moreover,
we show that our part models can help object detection performance by enriching
the R-CNN detector with parts
Learning Aerial Image Segmentation from Online Maps
This study deals with semantic segmentation of high-resolution (aerial)
images where a semantic class label is assigned to each pixel via supervised
classification as a basis for automatic map generation. Recently, deep
convolutional neural networks (CNNs) have shown impressive performance and have
quickly become the de-facto standard for semantic segmentation, with the added
benefit that task-specific feature design is no longer necessary. However, a
major downside of deep learning methods is that they are extremely data-hungry,
thus aggravating the perennial bottleneck of supervised classification, to
obtain enough annotated training data. On the other hand, it has been observed
that they are rather robust against noise in the training labels. This opens up
the intriguing possibility to avoid annotating huge amounts of training data,
and instead train the classifier from existing legacy data or crowd-sourced
maps which can exhibit high levels of noise. The question addressed in this
paper is: can training with large-scale, publicly available labels replace a
substantial part of the manual labeling effort and still achieve sufficient
performance? Such data will inevitably contain a significant portion of errors,
but in return virtually unlimited quantities of it are available in larger
parts of the world. We adapt a state-of-the-art CNN architecture for semantic
segmentation of buildings and roads in aerial images, and compare its
performance when using different training data sets, ranging from manually
labeled, pixel-accurate ground truth of the same city to automatic training
data derived from OpenStreetMap data from distant locations. We report our
results that indicate that satisfying performance can be obtained with
significantly less manual annotation effort, by exploiting noisy large-scale
training data.Comment: Published in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSIN
Learning Object Categories From Internet Image Searches
In this paper, we describe a simple approach to learning models of visual object categories from images gathered from Internet image search engines. The images for a given keyword are typically highly variable, with a large fraction being unrelated to the query term, and thus pose a challenging environment from which to learn. By training our models directly from Internet images, we remove the need to laboriously compile training data sets, required by most other recognition approaches-this opens up the possibility of learning object category models “on-the-fly.” We describe two simple approaches, derived from the probabilistic latent semantic analysis (pLSA) technique for text document analysis, that can be used to automatically learn object models from these data. We show two applications of the learned model: first, to rerank the images returned by the search engine, thus improving the quality of the search engine; and second, to recognize objects in other image data sets
Webly Supervised Learning of Convolutional Networks
We present an approach to utilize large amounts of web data for learning
CNNs. Specifically inspired by curriculum learning, we present a two-step
approach for CNN training. First, we use easy images to train an initial visual
representation. We then use this initial CNN and adapt it to harder, more
realistic images by leveraging the structure of data and categories. We
demonstrate that our two-stage CNN outperforms a fine-tuned CNN trained on
ImageNet on Pascal VOC 2012. We also demonstrate the strength of webly
supervised learning by localizing objects in web images and training a R-CNN
style detector. It achieves the best performance on VOC 2007 where no VOC
training data is used. Finally, we show our approach is quite robust to noise
and performs comparably even when we use image search results from March 2013
(pre-CNN image search era)
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
In this work we address the task of semantic image segmentation with Deep
Learning and make three main contributions that are experimentally shown to
have substantial practical merit. First, we highlight convolution with
upsampled filters, or 'atrous convolution', as a powerful tool in dense
prediction tasks. Atrous convolution allows us to explicitly control the
resolution at which feature responses are computed within Deep Convolutional
Neural Networks. It also allows us to effectively enlarge the field of view of
filters to incorporate larger context without increasing the number of
parameters or the amount of computation. Second, we propose atrous spatial
pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP
probes an incoming convolutional feature layer with filters at multiple
sampling rates and effective fields-of-views, thus capturing objects as well as
image context at multiple scales. Third, we improve the localization of object
boundaries by combining methods from DCNNs and probabilistic graphical models.
The commonly deployed combination of max-pooling and downsampling in DCNNs
achieves invariance but has a toll on localization accuracy. We overcome this
by combining the responses at the final DCNN layer with a fully connected
Conditional Random Field (CRF), which is shown both qualitatively and
quantitatively to improve localization performance. Our proposed "DeepLab"
system sets the new state-of-art at the PASCAL VOC-2012 semantic image
segmentation task, reaching 79.7% mIOU in the test set, and advances the
results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and
Cityscapes. All of our code is made publicly available online.Comment: Accepted by TPAM
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