5,273 research outputs found
Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering
Deep metric learning has been widely applied in many computer vision tasks,
and recently, it is more attractive in \emph{zero-shot image retrieval and
clustering}(ZSRC) where a good embedding is requested such that the unseen
classes can be distinguished well. Most existing works deem this 'good'
embedding just to be the discriminative one and thus race to devise powerful
metric objectives or hard-sample mining strategies for leaning discriminative
embedding. However, in this paper, we first emphasize that the generalization
ability is a core ingredient of this 'good' embedding as well and largely
affects the metric performance in zero-shot settings as a matter of fact. Then,
we propose the Energy Confused Adversarial Metric Learning(ECAML) framework to
explicitly optimize a robust metric. It is mainly achieved by introducing an
interesting Energy Confusion regularization term, which daringly breaks away
from the traditional metric learning idea of discriminative objective devising,
and seeks to 'confuse' the learned model so as to encourage its generalization
ability by reducing overfitting on the seen classes. We train this confusion
term together with the conventional metric objective in an adversarial manner.
Although it seems weird to 'confuse' the network, we show that our ECAML indeed
serves as an efficient regularization technique for metric learning and is
applicable to various conventional metric methods. This paper empirically and
experimentally demonstrates the importance of learning embedding with good
generalization, achieving state-of-the-art performances on the popular CUB,
CARS, Stanford Online Products and In-Shop datasets for ZSRC tasks.
\textcolor[rgb]{1, 0, 0}{Code available at http://www.bhchen.cn/}.Comment: AAAI 2019, Spotligh
-softmax: Improving Intra-class Compactness and Inter-class Separability of Features
Intra-class compactness and inter-class separability are crucial indicators
to measure the effectiveness of a model to produce discriminative features,
where intra-class compactness indicates how close the features with the same
label are to each other and inter-class separability indicates how far away the
features with different labels are. In this work, we investigate intra-class
compactness and inter-class separability of features learned by convolutional
networks and propose a Gaussian-based softmax (-softmax) function
that can effectively improve intra-class compactness and inter-class
separability. The proposed function is simple to implement and can easily
replace the softmax function. We evaluate the proposed -softmax
function on classification datasets (i.e., CIFAR-10, CIFAR-100, and Tiny
ImageNet) and on multi-label classification datasets (i.e., MS COCO and
NUS-WIDE). The experimental results show that the proposed
-softmax function improves the state-of-the-art models across all
evaluated datasets. In addition, analysis of the intra-class compactness and
inter-class separability demonstrates the advantages of the proposed function
over the softmax function, which is consistent with the performance
improvement. More importantly, we observe that high intra-class compactness and
inter-class separability are linearly correlated to average precision on MS
COCO and NUS-WIDE. This implies that improvement of intra-class compactness and
inter-class separability would lead to improvement of average precision.Comment: 15 pages, published in TNNL
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Remote sensing (RS) image retrieval is of great significant for geological
information mining. Over the past two decades, a large amount of research on
this task has been carried out, which mainly focuses on the following three
core issues: feature extraction, similarity metric and relevance feedback. Due
to the complexity and multiformity of ground objects in high-resolution remote
sensing (HRRS) images, there is still room for improvement in the current
retrieval approaches. In this paper, we analyze the three core issues of RS
image retrieval and provide a comprehensive review on existing methods.
Furthermore, for the goal to advance the state-of-the-art in HRRS image
retrieval, we focus on the feature extraction issue and delve how to use
powerful deep representations to address this task. We conduct systematic
investigation on evaluating correlative factors that may affect the performance
of deep features. By optimizing each factor, we acquire remarkable retrieval
results on publicly available HRRS datasets. Finally, we explain the
experimental phenomenon in detail and draw conclusions according to our
analysis. Our work can serve as a guiding role for the research of
content-based RS image retrieval
- …