Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dynamic Multi-Scale Loss Optimization for Object Detection   With the continuous improvement of the performance of object detectors via
advanced model architectures, imbalance problems in the training process have
received more attention. It is a common paradigm in object detection frameworks
to perform multi-scale detection. However, each scale is treated equally during
training. In this paper, we carefully study the objective imbalance of
multi-scale detector training. We argue that the loss in each scale level is
neither equally important nor independent. Different from the existing
solutions of setting multi-task weights, we dynamically optimize the loss
weight of each scale level in the training process. Specifically, we propose an
Adaptive Variance Weighting (AVW) to balance multi-scale loss according to the
statistical variance. Then we develop a novel Reinforcement Learning
Optimization (RLO) to decide the weighting scheme probabilistically during
training. The proposed dynamic methods make better utilization of multi-scale
training loss without extra computational complexity and learnable parameters
for backpropagation. Experiments show that our approaches can consistently
boost the performance over various baseline detectors on Pascal VOC and MS COCO
benchmark.

26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg