Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On zero-shot recognition of generic objects   Many recent advances in computer vision are the result of a healthy
competition among researchers on high quality, task-specific, benchmarks. After
a decade of active research, zero-shot learning (ZSL) models accuracy on the
Imagenet benchmark remains far too low to be considered for practical object
recognition applications. In this paper, we argue that the main reason behind
this apparent lack of progress is the poor quality of this benchmark. We
highlight major structural flaws of the current benchmark and analyze different
factors impacting the accuracy of ZSL models. We show that the actual
classification accuracy of existing ZSL models is significantly higher than was
previously thought as we account for these flaws. We then introduce the notion
of structural bias specific to ZSL datasets. We discuss how the presence of
this new form of bias allows for a trivial solution to the standard benchmark
and conclude on the need for a new benchmark. We then detail the semi-automated
construction of a new benchmark to address these flaws.

3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc