Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Coarse-To-Fine Incremental Few-Shot Learning   Different from fine-tuning models pre-trained on a large-scale dataset of
preset classes, class-incremental learning (CIL) aims to recognize novel
classes over time without forgetting pre-trained classes. However, a given
model will be challenged by test images with finer-grained classes, e.g., a
basenji is at most recognized as a dog. Such images form a new training set
(i.e., support set) so that the incremental model is hoped to recognize a
basenji (i.e., query) as a basenji next time. This paper formulates such a
hybrid natural problem of coarse-to-fine few-shot (C2FS) recognition as a CIL
problem named C2FSCIL, and proposes a simple, effective, and
theoretically-sound strategy Knowe: to learn, normalize, and freeze a
classifier's weights from fine labels, once learning an embedding space
contrastively from coarse labels. Besides, as CIL aims at a
stability-plasticity balance, new overall performance metrics are proposed. In
that sense, on CIFAR-100, BREEDS, and tieredImageNet, Knowe outperforms all
recent relevant CIL/FSCIL methods that are tailored to the new problem setting
for the first time.

34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol