46,610 research outputs found
Deep unsupervised clustering with Gaussian mixture variational autoencoders
We study a variant of the variational autoencoder model with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models. We observe that the standard variational approach in these models is unsuited for unsupervised clustering, and mitigate this problem by leveraging a principled information-theoretic regularisation term known as consistency violation. Adding this term to the standard variational optimisation objective yields networks with both meaningful internal representations and well-defined clusters. We demonstrate the performance of this scheme on synthetic data, MNIST and SVHN, showing that the obtained clusters are distinct, interpretable and result in achieving higher performance on unsupervised clustering classification than previous approaches
Im2Pano3D: Extrapolating 360 Structure and Semantics Beyond the Field of View
We present Im2Pano3D, a convolutional neural network that generates a dense
prediction of 3D structure and a probability distribution of semantic labels
for a full 360 panoramic view of an indoor scene when given only a partial
observation (<= 50%) in the form of an RGB-D image. To make this possible,
Im2Pano3D leverages strong contextual priors learned from large-scale synthetic
and real-world indoor scenes. To ease the prediction of 3D structure, we
propose to parameterize 3D surfaces with their plane equations and train the
model to predict these parameters directly. To provide meaningful training
supervision, we use multiple loss functions that consider both pixel level
accuracy and global context consistency. Experiments demon- strate that
Im2Pano3D is able to predict the semantics and 3D structure of the unobserved
scene with more than 56% pixel accuracy and less than 0.52m average distance
error, which is significantly better than alternative approaches.Comment: Video summary: https://youtu.be/Au3GmktK-S
Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction
of multiple target variables of diverse type. Due to its enormous application
potential, it has developed into an active and rapidly expanding research field
that combines several subfields of machine learning, including multivariate
regression, multi-label classification, multi-task learning, dyadic prediction,
zero-shot learning, network inference, and matrix completion. In this paper, we
present a unifying view on MTP problems and methods. First, we formally discuss
commonalities and differences between existing MTP problems. To this end, we
introduce a general framework that covers the above subfields as special cases.
As a second contribution, we provide a structured overview of MTP methods. This
is accomplished by identifying a number of key properties, which distinguish
such methods and determine their suitability for different types of problems.
Finally, we also discuss a few challenges for future research
Joint Multi-Person Pose Estimation and Semantic Part Segmentation
Human pose estimation and semantic part segmentation are two complementary
tasks in computer vision. In this paper, we propose to solve the two tasks
jointly for natural multi-person images, in which the estimated pose provides
object-level shape prior to regularize part segments while the part-level
segments constrain the variation of pose locations. Specifically, we first
train two fully convolutional neural networks (FCNs), namely Pose FCN and Part
FCN, to provide initial estimation of pose joint potential and semantic part
potential. Then, to refine pose joint location, the two types of potentials are
fused with a fully-connected conditional random field (FCRF), where a novel
segment-joint smoothness term is used to encourage semantic and spatial
consistency between parts and joints. To refine part segments, the refined pose
and the original part potential are integrated through a Part FCN, where the
skeleton feature from pose serves as additional regularization cues for part
segments. Finally, to reduce the complexity of the FCRF, we induce human
detection boxes and infer the graph inside each box, making the inference forty
times faster.
Since there's no dataset that contains both part segments and pose labels, we
extend the PASCAL VOC part dataset with human pose joints and perform extensive
experiments to compare our method against several most recent strategies. We
show that on this dataset our algorithm surpasses competing methods by a large
margin in both tasks.Comment: This paper has been accepted by CVPR 201
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