Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Representation Compensation Networks for Continual Semantic Segmentation   In this work, we study the continual semantic segmentation problem, where the
deep neural networks are required to incorporate new classes continually
without catastrophic forgetting. We propose to use a structural
re-parameterization mechanism, named representation compensation (RC) module,
to decouple the representation learning of both old and new knowledge. The RC
module consists of two dynamically evolved branches with one frozen and one
trainable. Besides, we design a pooled cube knowledge distillation strategy on
both spatial and channel dimensions to further enhance the plasticity and
stability of the model. We conduct experiments on two challenging continual
semantic segmentation scenarios, continual class segmentation and continual
domain segmentation. Without any extra computational overhead and parameters
during inference, our method outperforms state-of-the-art performance. The code
is available at \url{https://github.com/zhangchbin/RCIL}.

15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci