2,016 research outputs found
Separate Training for Conditional Random Fields Using Co-occurrence Rate Factorization
The standard training method of Conditional Random Fields (CRFs) is very slow
for large-scale applications. As an alternative, piecewise training divides the
full graph into pieces, trains them independently, and combines the learned
weights at test time. In this paper, we present \emph{separate} training for
undirected models based on the novel Co-occurrence Rate Factorization (CR-F).
Separate training is a local training method. In contrast to MEMMs, separate
training is unaffected by the label bias problem. Experiments show that
separate training (i) is unaffected by the label bias problem; (ii) reduces the
training time from weeks to seconds; and (iii) obtains competitive results to
the standard and piecewise training on linear-chain CRFs.Comment: 10page
Learning to segment with image-level supervision
Deep convolutional networks have achieved the state-of-the-art for semantic
image segmentation tasks. However, training these networks requires access to
densely labeled images, which are known to be very expensive to obtain. On the
other hand, the web provides an almost unlimited source of images annotated at
the image level. How can one utilize this much larger weakly annotated set for
tasks that require dense labeling? Prior work often relied on localization
cues, such as saliency maps, objectness priors, bounding boxes etc., to address
this challenging problem. In this paper, we propose a model that generates
auxiliary labels for each image, while simultaneously forcing the output of the
CNN to satisfy the mean-field constraints imposed by a conditional random
field. We show that one can enforce the CRF constraints by forcing the
distribution at each pixel to be close to the distribution of its neighbors.
This is in stark contrast with methods that compute a recursive expansion of
the mean-field distribution using a recurrent architecture and train the
resultant distribution. Instead, the proposed model adds an extra loss term to
the output of the CNN, and hence, is faster than recursive implementations. We
achieve the state-of-the-art for weakly supervised semantic image segmentation
on VOC 2012 dataset, assuming no manually labeled pixel level information is
available. Furthermore, the incorporation of conditional random fields in CNN
incurs little extra time during training.Comment: Published in WACV 201
Learning Edge Representations via Low-Rank Asymmetric Projections
We propose a new method for embedding graphs while preserving directed edge
information. Learning such continuous-space vector representations (or
embeddings) of nodes in a graph is an important first step for using network
information (from social networks, user-item graphs, knowledge bases, etc.) in
many machine learning tasks.
Unlike previous work, we (1) explicitly model an edge as a function of node
embeddings, and we (2) propose a novel objective, the "graph likelihood", which
contrasts information from sampled random walks with non-existent edges.
Individually, both of these contributions improve the learned representations,
especially when there are memory constraints on the total size of the
embeddings. When combined, our contributions enable us to significantly improve
the state-of-the-art by learning more concise representations that better
preserve the graph structure.
We evaluate our method on a variety of link-prediction task including social
networks, collaboration networks, and protein interactions, showing that our
proposed method learn representations with error reductions of up to 76% and
55%, on directed and undirected graphs. In addition, we show that the
representations learned by our method are quite space efficient, producing
embeddings which have higher structure-preserving accuracy but are 10 times
smaller
An ILP Solver for Multi-label MRFs with Connectivity Constraints
Integer Linear Programming (ILP) formulations of Markov random fields (MRFs)
models with global connectivity priors were investigated previously in computer
vision, e.g., \cite{globalinter,globalconn}. In these works, only Linear
Programing (LP) relaxations \cite{globalinter,globalconn} or simplified
versions \cite{graphcutbase} of the problem were solved. This paper
investigates the ILP of multi-label MRF with exact connectivity priors via a
branch-and-cut method, which provably finds globally optimal solutions. The
method enforces connectivity priors iteratively by a cutting plane method, and
provides feasible solutions with a guarantee on sub-optimality even if we
terminate it earlier. The proposed ILP can be applied as a post-processing
method on top of any existing multi-label segmentation approach. As it provides
globally optimal solution, it can be used off-line to generate ground-truth
labeling, which serves as quality check for any fast on-line algorithm.
Furthermore, it can be used to generate ground-truth proposals for weakly
supervised segmentation. We demonstrate the power and usefulness of our model
by several experiments on the BSDS500 and PASCAL image dataset, as well as on
medical images with trained probability maps.Comment: 19 page
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