Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SCOUTER: Slot Attention-based Classifier for Explainable Image
  Recognition   Explainable artificial intelligence has been gaining attention in the past
few years. However, most existing methods are based on gradients or
intermediate features, which are not directly involved in the decision-making
process of the classifier. In this paper, we propose a slot attention-based
classifier called SCOUTER for transparent yet accurate classification. Two
major differences from other attention-based methods include: (a) SCOUTER's
explanation is involved in the final confidence for each category, offering
more intuitive interpretation, and (b) all the categories have their
corresponding positive or negative explanation, which tells "why the image is
of a certain category" or "why the image is not of a certain category." We
design a new loss tailored for SCOUTER that controls the model's behavior to
switch between positive and negative explanations, as well as the size of
explanatory regions. Experimental results show that SCOUTER can give better
visual explanations in terms of various metrics while keeping good accuracy on
small and medium-sized datasets.

17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph