10,665 research outputs found
Deep Descriptor Transforming for Image Co-Localization
Reusable model design becomes desirable with the rapid expansion of machine
learning applications. In this paper, we focus on the reusability of
pre-trained deep convolutional models. Specifically, different from treating
pre-trained models as feature extractors, we reveal more treasures beneath
convolutional layers, i.e., the convolutional activations could act as a
detector for the common object in the image co-localization problem. We propose
a simple but effective method, named Deep Descriptor Transforming (DDT), for
evaluating the correlations of descriptors and then obtaining the
category-consistent regions, which can accurately locate the common object in a
set of images. Empirical studies validate the effectiveness of the proposed DDT
method. On benchmark image co-localization datasets, DDT consistently
outperforms existing state-of-the-art methods by a large margin. Moreover, DDT
also demonstrates good generalization ability for unseen categories and
robustness for dealing with noisy data.Comment: Accepted by IJCAI 201
Multi-scale Orderless Pooling of Deep Convolutional Activation Features
Deep convolutional neural networks (CNN) have shown their promise as a
universal representation for recognition. However, global CNN activations lack
geometric invariance, which limits their robustness for classification and
matching of highly variable scenes. To improve the invariance of CNN
activations without degrading their discriminative power, this paper presents a
simple but effective scheme called multi-scale orderless pooling (MOP-CNN).
This scheme extracts CNN activations for local patches at multiple scale
levels, performs orderless VLAD pooling of these activations at each level
separately, and concatenates the result. The resulting MOP-CNN representation
can be used as a generic feature for either supervised or unsupervised
recognition tasks, from image classification to instance-level retrieval; it
consistently outperforms global CNN activations without requiring any joint
training of prediction layers for a particular target dataset. In absolute
terms, it achieves state-of-the-art results on the challenging SUN397 and MIT
Indoor Scenes classification datasets, and competitive results on
ILSVRC2012/2013 classification and INRIA Holidays retrieval datasets
Stratified Transfer Learning for Cross-domain Activity Recognition
In activity recognition, it is often expensive and time-consuming to acquire
sufficient activity labels. To solve this problem, transfer learning leverages
the labeled samples from the source domain to annotate the target domain which
has few or none labels. Existing approaches typically consider learning a
global domain shift while ignoring the intra-affinity between classes, which
will hinder the performance of the algorithms. In this paper, we propose a
novel and general cross-domain learning framework that can exploit the
intra-affinity of classes to perform intra-class knowledge transfer. The
proposed framework, referred to as Stratified Transfer Learning (STL), can
dramatically improve the classification accuracy for cross-domain activity
recognition. Specifically, STL first obtains pseudo labels for the target
domain via majority voting technique. Then, it performs intra-class knowledge
transfer iteratively to transform both domains into the same subspaces.
Finally, the labels of target domain are obtained via the second annotation. To
evaluate the performance of STL, we conduct comprehensive experiments on three
large public activity recognition datasets~(i.e. OPPORTUNITY, PAMAP2, and UCI
DSADS), which demonstrates that STL significantly outperforms other
state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%).
Furthermore, we extensively investigate the performance of STL across different
degrees of similarities and activity levels between domains. And we also
discuss the potential of STL in other pervasive computing applications to
provide empirical experience for future research.Comment: 10 pages; accepted by IEEE PerCom 2018; full paper. (camera-ready
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