Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Lightweight Pedestrian Detector with Hierarchical Knowledge
  Distillation   It remains very challenging to build a pedestrian detection system for real
world applications, which demand for both accuracy and speed. This work
presents a novel hierarchical knowledge distillation framework to learn a
lightweight pedestrian detector, which significantly reduces the computational
cost and still holds the high accuracy at the same time. Following the
`teacher--student' diagram that a stronger, deeper neural network can teach a
lightweight network to learn better representations, we explore multiple
knowledge distillation architectures and reframe this approach as a unified,
hierarchical distillation framework. In particular, the proposed distillation
is performed at multiple hierarchies, multiple stages in a modern detector,
which empowers the student detector to learn both low-level details and
high-level abstractions simultaneously. Experiment result shows that a student
model trained by our framework, with 6 times compression in number of
parameters, still achieves competitive performance as the teacher model on the
widely used pedestrian detection benchmark.

34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn