35,663 research outputs found
Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification
In order to encode the class correlation and class specific information in
image representation, we propose a new local feature learning approach named
Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to
hierarchically learn feature transformation filter banks to transform raw pixel
image patches to features. The learned filter banks are expected to: (1) encode
common visual patterns of a flexible number of categories; (2) encode
discriminative information; and (3) hierarchically extract patterns at
different visual levels. Particularly, in each single layer of DDSFL, shareable
filters are jointly learned for classes which share the similar patterns.
Discriminative power of the filters is achieved by enforcing the features from
the same category to be close, while features from different categories to be
far away from each other. Furthermore, we also propose two exemplar selection
methods to iteratively select training data for more efficient and effective
learning. Based on the experimental results, DDSFL can achieve very promising
performance, and it also shows great complementary effect to the
state-of-the-art Caffe features.Comment: Pattern Recognition, Elsevier, 201
iCaRL: Incremental Classifier and Representation Learning
A major open problem on the road to artificial intelligence is the
development of incrementally learning systems that learn about more and more
concepts over time from a stream of data. In this work, we introduce a new
training strategy, iCaRL, that allows learning in such a class-incremental way:
only the training data for a small number of classes has to be present at the
same time and new classes can be added progressively. iCaRL learns strong
classifiers and a data representation simultaneously. This distinguishes it
from earlier works that were fundamentally limited to fixed data
representations and therefore incompatible with deep learning architectures. We
show by experiments on CIFAR-100 and ImageNet ILSVRC 2012 data that iCaRL can
learn many classes incrementally over a long period of time where other
strategies quickly fail.Comment: Accepted paper at CVPR 201
Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views
This paper presents an end-to-end convolutional neural network (CNN) for
2D-3D exemplar detection. We demonstrate that the ability to adapt the features
of natural images to better align with those of CAD rendered views is critical
to the success of our technique. We show that the adaptation can be learned by
compositing rendered views of textured object models on natural images. Our
approach can be naturally incorporated into a CNN detection pipeline and
extends the accuracy and speed benefits from recent advances in deep learning
to 2D-3D exemplar detection. We applied our method to two tasks: instance
detection, where we evaluated on the IKEA dataset, and object category
detection, where we out-perform Aubry et al. for "chair" detection on a subset
of the Pascal VOC dataset.Comment: To appear in CVPR 201
Deep Unsupervised Similarity Learning using Partially Ordered Sets
Unsupervised learning of visual similarities is of paramount importance to
computer vision, particularly due to lacking training data for fine-grained
similarities. Deep learning of similarities is often based on relationships
between pairs or triplets of samples. Many of these relations are unreliable
and mutually contradicting, implying inconsistencies when trained without
supervision information that relates different tuples or triplets to each
other. To overcome this problem, we use local estimates of reliable
(dis-)similarities to initially group samples into compact surrogate classes
and use local partial orders of samples to classes to link classes to each
other. Similarity learning is then formulated as a partial ordering task with
soft correspondences of all samples to classes. Adopting a strategy of
self-supervision, a CNN is trained to optimally represent samples in a mutually
consistent manner while updating the classes. The similarity learning and
grouping procedure are integrated in a single model and optimized jointly. The
proposed unsupervised approach shows competitive performance on detailed pose
estimation and object classification.Comment: Accepted for publication at IEEE Computer Vision and Pattern
Recognition 201
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Abstraction and context in concept representation
This paper develops the notion of abstraction in the context of the psychology of concepts, and discusses its relation to context dependence in knowledge representation. Three general approaches to modelling conceptual knowledge from the domain of cognitive psychology are discussed, which serve to illustrate a theoretical dimension of increasing levels of abstraction
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