110,496 research outputs found
Neighbourhood Consensus Networks
We address the problem of finding reliable dense correspondences between a
pair of images. This is a challenging task due to strong appearance differences
between the corresponding scene elements and ambiguities generated by
repetitive patterns. The contributions of this work are threefold. First,
inspired by the classic idea of disambiguating feature matches using semi-local
constraints, we develop an end-to-end trainable convolutional neural network
architecture that identifies sets of spatially consistent matches by analyzing
neighbourhood consensus patterns in the 4D space of all possible
correspondences between a pair of images without the need for a global
geometric model. Second, we demonstrate that the model can be trained
effectively from weak supervision in the form of matching and non-matching
image pairs without the need for costly manual annotation of point to point
correspondences. Third, we show the proposed neighbourhood consensus network
can be applied to a range of matching tasks including both category- and
instance-level matching, obtaining the state-of-the-art results on the PF
Pascal dataset and the InLoc indoor visual localization benchmark.Comment: In Proceedings of the 32nd Conference on Neural Information
Processing Systems (NeurIPS 2018
Human Pose Estimation using Deep Consensus Voting
In this paper we consider the problem of human pose estimation from a single
still image. We propose a novel approach where each location in the image votes
for the position of each keypoint using a convolutional neural net. The voting
scheme allows us to utilize information from the whole image, rather than rely
on a sparse set of keypoint locations. Using dense, multi-target votes, not
only produces good keypoint predictions, but also enables us to compute
image-dependent joint keypoint probabilities by looking at consensus voting.
This differs from most previous methods where joint probabilities are learned
from relative keypoint locations and are independent of the image. We finally
combine the keypoints votes and joint probabilities in order to identify the
optimal pose configuration. We show our competitive performance on the MPII
Human Pose and Leeds Sports Pose datasets
Learning how to be robust: Deep polynomial regression
Polynomial regression is a recurrent problem with a large number of
applications. In computer vision it often appears in motion analysis. Whatever
the application, standard methods for regression of polynomial models tend to
deliver biased results when the input data is heavily contaminated by outliers.
Moreover, the problem is even harder when outliers have strong structure.
Departing from problem-tailored heuristics for robust estimation of parametric
models, we explore deep convolutional neural networks. Our work aims to find a
generic approach for training deep regression models without the explicit need
of supervised annotation. We bypass the need for a tailored loss function on
the regression parameters by attaching to our model a differentiable hard-wired
decoder corresponding to the polynomial operation at hand. We demonstrate the
value of our findings by comparing with standard robust regression methods.
Furthermore, we demonstrate how to use such models for a real computer vision
problem, i.e., video stabilization. The qualitative and quantitative
experiments show that neural networks are able to learn robustness for general
polynomial regression, with results that well overpass scores of traditional
robust estimation methods.Comment: 18 pages, conferenc
Deep Poselets for Human Detection
We address the problem of detecting people in natural scenes using a part
approach based on poselets. We propose a bootstrapping method that allows us to
collect millions of weakly labeled examples for each poselet type. We use these
examples to train a Convolutional Neural Net to discriminate different poselet
types and separate them from the background class. We then use the trained CNN
as a way to represent poselet patches with a Pose Discriminative Feature (PDF)
vector -- a compact 256-dimensional feature vector that is effective at
discriminating pose from appearance. We train the poselet model on top of PDF
features and combine them with object-level CNNs for detection and bounding box
prediction. The resulting model leads to state-of-the-art performance for human
detection on the PASCAL datasets
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
We address the unsupervised learning of several interconnected problems in
low-level vision: single view depth prediction, camera motion estimation,
optical flow, and segmentation of a video into the static scene and moving
regions. Our key insight is that these four fundamental vision problems are
coupled through geometric constraints. Consequently, learning to solve them
together simplifies the problem because the solutions can reinforce each other.
We go beyond previous work by exploiting geometry more explicitly and
segmenting the scene into static and moving regions. To that end, we introduce
Competitive Collaboration, a framework that facilitates the coordinated
training of multiple specialized neural networks to solve complex problems.
Competitive Collaboration works much like expectation-maximization, but with
neural networks that act as both competitors to explain pixels that correspond
to static or moving regions, and as collaborators through a moderator that
assigns pixels to be either static or independently moving. Our novel method
integrates all these problems in a common framework and simultaneously reasons
about the segmentation of the scene into moving objects and the static
background, the camera motion, depth of the static scene structure, and the
optical flow of moving objects. Our model is trained without any supervision
and achieves state-of-the-art performance among joint unsupervised methods on
all sub-problems.Comment: CVPR 201
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