615 research outputs found
Developing pedagogic theory: the case of geometry proof teaching
This paper compares the teaching of proof in geometry at the lower secondary school level in the East (China, Japan) and in the West (UK). The aim is to seek to identify teaching strategies that might inform new pedagogic approaches for teaching deductive proof and proving. In the West, much theory focuses on examining the nature of classroom tasks. In the East, the heuristic nature of teaching and the theory of variation are useful as they focus on the dynamic role of the teacher. The paper suggests that the main need is for deeper thinking on the relationship between teachers’ instructional practices and the development of students’ mathematical reasoning
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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