8,979 research outputs found
Classification-Specific Parts for Improving Fine-Grained Visual Categorization
Fine-grained visual categorization is a classification task for
distinguishing categories with high intra-class and small inter-class variance.
While global approaches aim at using the whole image for performing the
classification, part-based solutions gather additional local information in
terms of attentions or parts. We propose a novel classification-specific part
estimation that uses an initial prediction as well as back-propagation of
feature importance via gradient computations in order to estimate relevant
image regions. The subsequently detected parts are then not only selected by
a-posteriori classification knowledge, but also have an intrinsic spatial
extent that is determined automatically. This is in contrast to most part-based
approaches and even to available ground-truth part annotations, which only
provide point coordinates and no additional scale information. We show in our
experiments on various widely-used fine-grained datasets the effectiveness of
the mentioned part selection method in conjunction with the extracted part
features.Comment: Presented at the GCPR201
No Spare Parts: Sharing Part Detectors for Image Categorization
This work aims for image categorization using a representation of distinctive
parts. Different from existing part-based work, we argue that parts are
naturally shared between image categories and should be modeled as such. We
motivate our approach with a quantitative and qualitative analysis by
backtracking where selected parts come from. Our analysis shows that in
addition to the category parts defining the class, the parts coming from the
background context and parts from other image categories improve categorization
performance. Part selection should not be done separately for each category,
but instead be shared and optimized over all categories. To incorporate part
sharing between categories, we present an algorithm based on AdaBoost to
jointly optimize part sharing and selection, as well as fusion with the global
image representation. We achieve results competitive to the state-of-the-art on
object, scene, and action categories, further improving over deep convolutional
neural networks
Subset Feature Learning for Fine-Grained Category Classification
Fine-grained categorisation has been a challenging problem due to small
inter-class variation, large intra-class variation and low number of training
images. We propose a learning system which first clusters visually similar
classes and then learns deep convolutional neural network features specific to
each subset. Experiments on the popular fine-grained Caltech-UCSD bird dataset
show that the proposed method outperforms recent fine-grained categorisation
methods under the most difficult setting: no bounding boxes are presented at
test time. It achieves a mean accuracy of 77.5%, compared to the previous best
performance of 73.2%. We also show that progressive transfer learning allows us
to first learn domain-generic features (for bird classification) which can then
be adapted to specific set of bird classes, yielding improvements in accuracy
Pairwise Confusion for Fine-Grained Visual Classification
Fine-Grained Visual Classification (FGVC) datasets contain small sample
sizes, along with significant intra-class variation and inter-class similarity.
While prior work has addressed intra-class variation using localization and
segmentation techniques, inter-class similarity may also affect feature
learning and reduce classification performance. In this work, we address this
problem using a novel optimization procedure for the end-to-end neural network
training on FGVC tasks. Our procedure, called Pairwise Confusion (PC) reduces
overfitting by intentionally {introducing confusion} in the activations. With
PC regularization, we obtain state-of-the-art performance on six of the most
widely-used FGVC datasets and demonstrate improved localization ability. {PC}
is easy to implement, does not need excessive hyperparameter tuning during
training, and does not add significant overhead during test time.Comment: Camera-Ready version for ECCV 201
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