Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Few-Shot Learning with Localization in Realistic Settings   Traditional recognition methods typically require large,
artificially-balanced training classes, while few-shot learning methods are
tested on artificially small ones. In contrast to both extremes, real world
recognition problems exhibit heavy-tailed class distributions, with cluttered
scenes and a mix of coarse and fine-grained class distinctions. We show that
prior methods designed for few-shot learning do not work out of the box in
these challenging conditions, based on a new "meta-iNat" benchmark. We
introduce three parameter-free improvements: (a) better training procedures
based on adapting cross-validation to meta-learning, (b) novel architectures
that localize objects using limited bounding box annotations before
classification, and (c) simple parameter-free expansions of the feature space
based on bilinear pooling. Together, these improvements double the accuracy of
state-of-the-art models on meta-iNat while generalizing to prior benchmarks,
complex neural architectures, and settings with substantial domain shift.

37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph