26,745 research outputs found
Learning Multimodal Latent Attributes
Abstract—The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multi-modal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we (1) introduce a concept of semi-latent attribute space, expressing user-defined and latent attributes in a unified framework, and (2) propose a novel scalable probabilistic topic model for learning multi-modal semi-latent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multi-task learning, learning with label noise, N-shot transfer learning and importantly zero-shot learning
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Curriculum semi-supervised segmentation
This study investigates a curriculum-style strategy for semi-supervised CNN
segmentation, which devises a regression network to learn image-level
information such as the size of a target region. These regressions are used to
effectively regularize the segmentation network, constraining softmax
predictions of the unlabeled images to match the inferred label distributions.
Our framework is based on inequality constraints that tolerate uncertainties
with inferred knowledge, e.g., regressed region size, and can be employed for a
large variety of region attributes. We evaluated our proposed strategy for left
ventricle segmentation in magnetic resonance images (MRI), and compared it to
standard proposal-based semi-supervision strategies. Our strategy leverages
unlabeled data in more efficiently, and achieves very competitive results,
approaching the performance of full-supervision.Comment: Accepted as paper as MICCAI 2O1
Semantic Autoencoder for Zero-Shot Learning
Existing zero-shot learning (ZSL) models typically learn a projection
function from a feature space to a semantic embedding space (e.g.~attribute
space). However, such a projection function is only concerned with predicting
the training seen class semantic representation (e.g.~attribute prediction) or
classification. When applied to test data, which in the context of ZSL contains
different (unseen) classes without training data, a ZSL model typically suffers
from the project domain shift problem. In this work, we present a novel
solution to ZSL based on learning a Semantic AutoEncoder (SAE). Taking the
encoder-decoder paradigm, an encoder aims to project a visual feature vector
into the semantic space as in the existing ZSL models. However, the decoder
exerts an additional constraint, that is, the projection/code must be able to
reconstruct the original visual feature. We show that with this additional
reconstruction constraint, the learned projection function from the seen
classes is able to generalise better to the new unseen classes. Importantly,
the encoder and decoder are linear and symmetric which enable us to develop an
extremely efficient learning algorithm. Extensive experiments on six benchmark
datasets demonstrate that the proposed SAE outperforms significantly the
existing ZSL models with the additional benefit of lower computational cost.
Furthermore, when the SAE is applied to supervised clustering problem, it also
beats the state-of-the-art.Comment: accepted to CVPR201
End-to-End Localization and Ranking for Relative Attributes
We propose an end-to-end deep convolutional network to simultaneously
localize and rank relative visual attributes, given only weakly-supervised
pairwise image comparisons. Unlike previous methods, our network jointly learns
the attribute's features, localization, and ranker. The localization module of
our network discovers the most informative image region for the attribute,
which is then used by the ranking module to learn a ranking model of the
attribute. Our end-to-end framework also significantly speeds up processing and
is much faster than previous methods. We show state-of-the-art ranking results
on various relative attribute datasets, and our qualitative localization
results clearly demonstrate our network's ability to learn meaningful image
patches.Comment: Appears in European Conference on Computer Vision (ECCV), 201
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