Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning   Generalized few-shot semantic segmentation was introduced to move beyond only
evaluating few-shot segmentation models on novel classes to include testing
their ability to remember base classes. While the current state-of-the-art
approach is based on meta-learning, it performs poorly and saturates in
learning after observing only a few shots. We propose the first fine-tuning
solution, and demonstrate that it addresses the saturation problem while
achieving state-of-the-art results on two datasets, PASCAL-5i and COCO-20i. We
also show that it outperforms existing methods, whether fine-tuning multiple
final layers or only the final layer. Finally, we present a triplet loss
regularization that shows how to redistribute the balance of performance
between novel and base categories so that there is a smaller gap between them.

2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga