7,619 research outputs found
End-to-end weakly-supervised semantic alignment
We tackle the task of semantic alignment where the goal is to compute dense
semantic correspondence aligning two images depicting objects of the same
category. This is a challenging task due to large intra-class variation,
changes in viewpoint and background clutter. We present the following three
principal contributions. First, we develop a convolutional neural network
architecture for semantic alignment that is trainable in an end-to-end manner
from weak image-level supervision in the form of matching image pairs. The
outcome is that parameters are learnt from rich appearance variation present in
different but semantically related images without the need for tedious manual
annotation of correspondences at training time. Second, the main component of
this architecture is a differentiable soft inlier scoring module, inspired by
the RANSAC inlier scoring procedure, that computes the quality of the alignment
based on only geometrically consistent correspondences thereby reducing the
effect of background clutter. Third, we demonstrate that the proposed approach
achieves state-of-the-art performance on multiple standard benchmarks for
semantic alignment.Comment: In 2018 IEEE Conference on Computer Vision and Pattern Recognition
(CVPR 2018
Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency
In this paper, we introduce a novel unsupervised domain adaptation technique
for the task of 3D keypoint prediction from a single depth scan or image. Our
key idea is to utilize the fact that predictions from different views of the
same or similar objects should be consistent with each other. Such view
consistency can provide effective regularization for keypoint prediction on
unlabeled instances. In addition, we introduce a geometric alignment term to
regularize predictions in the target domain. The resulting loss function can be
effectively optimized via alternating minimization. We demonstrate the
effectiveness of our approach on real datasets and present experimental results
showing that our approach is superior to state-of-the-art general-purpose
domain adaptation techniques.Comment: ECCV 201
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
Unsupervised learning of object landmarks by factorized spatial embeddings
Learning automatically the structure of object categories remains an
important open problem in computer vision. In this paper, we propose a novel
unsupervised approach that can discover and learn landmarks in object
categories, thus characterizing their structure. Our approach is based on
factorizing image deformations, as induced by a viewpoint change or an object
deformation, by learning a deep neural network that detects landmarks
consistently with such visual effects. Furthermore, we show that the learned
landmarks establish meaningful correspondences between different object
instances in a category without having to impose this requirement explicitly.
We assess the method qualitatively on a variety of object types, natural and
man-made. We also show that our unsupervised landmarks are highly predictive of
manually-annotated landmarks in face benchmark datasets, and can be used to
regress these with a high degree of accuracy.Comment: To be published in ICCV 201
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