1,194 research outputs found
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
A Deep Pyramid Deformable Part Model for Face Detection
We present a face detection algorithm based on Deformable Part Models and
deep pyramidal features. The proposed method called DP2MFD is able to detect
faces of various sizes and poses in unconstrained conditions. It reduces the
gap in training and testing of DPM on deep features by adding a normalization
layer to the deep convolutional neural network (CNN). Extensive experiments on
four publicly available unconstrained face detection datasets show that our
method is able to capture the meaningful structure of faces and performs
significantly better than many competitive face detection algorithms
Grid Loss: Detecting Occluded Faces
Detection of partially occluded objects is a challenging computer vision
problem. Standard Convolutional Neural Network (CNN) detectors fail if parts of
the detection window are occluded, since not every sub-part of the window is
discriminative on its own. To address this issue, we propose a novel loss layer
for CNNs, named grid loss, which minimizes the error rate on sub-blocks of a
convolution layer independently rather than over the whole feature map. This
results in parts being more discriminative on their own, enabling the detector
to recover if the detection window is partially occluded. By mapping our loss
layer back to a regular fully connected layer, no additional computational cost
is incurred at runtime compared to standard CNNs. We demonstrate our method for
face detection on several public face detection benchmarks and show that our
method outperforms regular CNNs, is suitable for realtime applications and
achieves state-of-the-art performance.Comment: accepted to ECCV 201
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
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