8,805 research outputs found
The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification
Fine-grained classification is challenging because categories can only be
discriminated by subtle and local differences. Variances in the pose, scale or
rotation usually make the problem more difficult. Most fine-grained
classification systems follow the pipeline of finding foreground object or
object parts (where) to extract discriminative features (what).
In this paper, we propose to apply visual attention to fine-grained
classification task using deep neural network. Our pipeline integrates three
types of attention: the bottom-up attention that propose candidate patches, the
object-level top-down attention that selects relevant patches to a certain
object, and the part-level top-down attention that localizes discriminative
parts. We combine these attentions to train domain-specific deep nets, then use
it to improve both the what and where aspects. Importantly, we avoid using
expensive annotations like bounding box or part information from end-to-end.
The weak supervision constraint makes our work easier to generalize.
We have verified the effectiveness of the method on the subsets of ILSVRC2012
dataset and CUB200_2011 dataset. Our pipeline delivered significant
improvements and achieved the best accuracy under the weakest supervision
condition. The performance is competitive against other methods that rely on
additional annotations
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN
Discriminative localization is essential for fine-grained image
classification task, which devotes to recognizing hundreds of subcategories in
the same basic-level category. Reflecting on discriminative regions of objects,
key differences among different subcategories are subtle and local. Existing
methods generally adopt a two-stage learning framework: The first stage is to
localize the discriminative regions of objects, and the second is to encode the
discriminative features for training classifiers. However, these methods
generally have two limitations: (1) Separation of the two-stage learning is
time-consuming. (2) Dependence on object and parts annotations for
discriminative localization learning leads to heavily labor-consuming labeling.
It is highly challenging to address these two important limitations
simultaneously. Existing methods only focus on one of them. Therefore, this
paper proposes the discriminative localization approach via saliency-guided
Faster R-CNN to address the above two limitations at the same time, and our
main novelties and advantages are: (1) End-to-end network based on Faster R-CNN
is designed to simultaneously localize discriminative regions and encode
discriminative features, which accelerates classification speed. (2)
Saliency-guided localization learning is proposed to localize the
discriminative region automatically, avoiding labor-consuming labeling. Both
are jointly employed to simultaneously accelerate classification speed and
eliminate dependence on object and parts annotations. Comparing with the
state-of-the-art methods on the widely-used CUB-200-2011 dataset, our approach
achieves both the best classification accuracy and efficiency.Comment: 9 pages, to appear in ACM MM 201
Dual Skipping Networks
Inspired by the recent neuroscience studies on the left-right asymmetry of
the human brain in processing low and high spatial frequency information, this
paper introduces a dual skipping network which carries out coarse-to-fine
object categorization. Such a network has two branches to simultaneously deal
with both coarse and fine-grained classification tasks. Specifically, we
propose a layer-skipping mechanism that learns a gating network to predict
which layers to skip in the testing stage. This layer-skipping mechanism endows
the network with good flexibility and capability in practice. Evaluations are
conducted on several widely used coarse-to-fine object categorization
benchmarks, and promising results are achieved by our proposed network model.Comment: CVPR 2018 (poster); fix typ
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