3,385 research outputs found
AnchorNet: A Weakly Supervised Network to Learn Geometry-sensitive Features For Semantic Matching
Despite significant progress of deep learning in recent years,
state-of-the-art semantic matching methods still rely on legacy features such
as SIFT or HoG. We argue that the strong invariance properties that are key to
the success of recent deep architectures on the classification task make them
unfit for dense correspondence tasks, unless a large amount of supervision is
used. In this work, we propose a deep network, termed AnchorNet, that produces
image representations that are well-suited for semantic matching. It relies on
a set of filters whose response is geometrically consistent across different
object instances, even in the presence of strong intra-class, scale, or
viewpoint variations. Trained only with weak image-level labels, the final
representation successfully captures information about the object structure and
improves results of state-of-the-art semantic matching methods such as the
deformable spatial pyramid or the proposal flow methods. We show positive
results on the cross-instance matching task where different instances of the
same object category are matched as well as on a new cross-category semantic
matching task aligning pairs of instances each from a different object class.Comment: Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition. 201
Bringing Background into the Foreground: Making All Classes Equal in Weakly-supervised Video Semantic Segmentation
Pixel-level annotations are expensive and time-consuming to obtain. Hence,
weak supervision using only image tags could have a significant impact in
semantic segmentation. Recent years have seen great progress in
weakly-supervised semantic segmentation, whether from a single image or from
videos. However, most existing methods are designed to handle a single
background class. In practical applications, such as autonomous navigation, it
is often crucial to reason about multiple background classes. In this paper, we
introduce an approach to doing so by making use of classifier heatmaps. We then
develop a two-stream deep architecture that jointly leverages appearance and
motion, and design a loss based on our heatmaps to train it. Our experiments
demonstrate the benefits of our classifier heatmaps and of our two-stream
architecture on challenging urban scene datasets and on the YouTube-Objects
benchmark, where we obtain state-of-the-art results.Comment: 11 pages, 4 figures, 7 tables, Accepted in ICCV 201
Sample and Filter: Nonparametric Scene Parsing via Efficient Filtering
Scene parsing has attracted a lot of attention in computer vision. While
parametric models have proven effective for this task, they cannot easily
incorporate new training data. By contrast, nonparametric approaches, which
bypass any learning phase and directly transfer the labels from the training
data to the query images, can readily exploit new labeled samples as they
become available. Unfortunately, because of the computational cost of their
label transfer procedures, state-of-the-art nonparametric methods typically
filter out most training images to only keep a few relevant ones to label the
query. As such, these methods throw away many images that still contain
valuable information and generally obtain an unbalanced set of labeled samples.
In this paper, we introduce a nonparametric approach to scene parsing that
follows a sample-and-filter strategy. More specifically, we propose to sample
labeled superpixels according to an image similarity score, which allows us to
obtain a balanced set of samples. We then formulate label transfer as an
efficient filtering procedure, which lets us exploit more labeled samples than
existing techniques. Our experiments evidence the benefits of our approach over
state-of-the-art nonparametric methods on two benchmark datasets.Comment: Please refer to the CVPR-2016 version of this manuscrip
Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks
Early and accurate diagnosis of interstitial lung diseases (ILDs) is crucial
for making treatment decisions, but can be challenging even for experienced
radiologists. The diagnostic procedure is based on the detection and
recognition of the different ILD pathologies in thoracic CT scans, yet their
manifestation often appears similar. In this study, we propose the use of a
deep purely convolutional neural network for the semantic segmentation of ILD
patterns, as the basic component of a computer aided diagnosis (CAD) system for
ILDs. The proposed CNN, which consists of convolutional layers with dilated
filters, takes as input a lung CT image of arbitrary size and outputs the
corresponding label map. We trained and tested the network on a dataset of 172
sparsely annotated CT scans, within a cross-validation scheme. The training was
performed in an end-to-end and semi-supervised fashion, utilizing both labeled
and non-labeled image regions. The experimental results show significant
performance improvement with respect to the state of the art
LR-CNN: Local-aware Region CNN for Vehicle Detection in Aerial Imagery
State-of-the-art object detection approaches such as Fast/Faster R-CNN, SSD,
or YOLO have difficulties detecting dense, small targets with arbitrary
orientation in large aerial images. The main reason is that using interpolation
to align RoI features can result in a lack of accuracy or even loss of location
information. We present the Local-aware Region Convolutional Neural Network
(LR-CNN), a novel two-stage approach for vehicle detection in aerial imagery.
We enhance translation invariance to detect dense vehicles and address the
boundary quantization issue amongst dense vehicles by aggregating the
high-precision RoIs' features. Moreover, we resample high-level semantic pooled
features, making them regain location information from the features of a
shallower convolutional block. This strengthens the local feature invariance
for the resampled features and enables detecting vehicles in an arbitrary
orientation. The local feature invariance enhances the learning ability of the
focal loss function, and the focal loss further helps to focus on the hard
examples. Taken together, our method better addresses the challenges of aerial
imagery. We evaluate our approach on several challenging datasets (VEDAI,
DOTA), demonstrating a significant improvement over state-of-the-art methods.
We demonstrate the good generalization ability of our approach on the DLR 3K
dataset.Comment: 8 page
DCTM: Discrete-Continuous Transformation Matching for Semantic Flow
Techniques for dense semantic correspondence have provided limited ability to
deal with the geometric variations that commonly exist between semantically
similar images. While variations due to scale and rotation have been examined,
there lack practical solutions for more complex deformations such as affine
transformations because of the tremendous size of the associated solution
space. To address this problem, we present a discrete-continuous transformation
matching (DCTM) framework where dense affine transformation fields are inferred
through a discrete label optimization in which the labels are iteratively
updated via continuous regularization. In this way, our approach draws
solutions from the continuous space of affine transformations in a manner that
can be computed efficiently through constant-time edge-aware filtering and a
proposed affine-varying CNN-based descriptor. Experimental results show that
this model outperforms the state-of-the-art methods for dense semantic
correspondence on various benchmarks
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