381 research outputs found
DISCUSSION ON THE DEVELOPMENT MODEL OF FOREST HEALTH BASE FROM THE PERSPECTIVE OF CONSUMER PSYCHOLOGY
RESEARCH ON THE PLANNING AND DESIGN OF FOREST HEALTH BASE BASED ON THE CONVALESCENT NEEDS OF PEOPLE WITH MENTAL DISORDERS
REFORM AND PRACTICE OF TEACHING METHODS OF GARDEN ART PRINCIPLES UNDER THE BACKGROUND OF EDUCATIONAL PSYCHOLOGY
RESEARCH ON THE PLANNING AND DESIGN OF FOREST HEALTH BASE BASED ON THE CONVALESCENT NEEDS OF PEOPLE WITH MENTAL DISORDERS
DISCUSSION ON THE DEVELOPMENT MODEL OF FOREST HEALTH BASE FROM THE PERSPECTIVE OF CONSUMER PSYCHOLOGY
REFORM AND PRACTICE OF TEACHING METHODS OF GARDEN ART PRINCIPLES UNDER THE BACKGROUND OF EDUCATIONAL PSYCHOLOGY
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
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