Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating
  Noisy Samples and Utilizing Hard Ones   Labeling objects at a subordinate level typically requires expert knowledge,
which is not always available when using random annotators. As such, learning
directly from web images for fine-grained recognition has attracted broad
attention. However, the presence of label noise and hard examples in web images
are two obstacles for training robust fine-grained recognition models.
Therefore, in this paper, we propose a novel approach for removing irrelevant
samples from real-world web images during training, while employing useful hard
examples to update the network. Thus, our approach can alleviate the harmful
effects of irrelevant noisy web images and hard examples to achieve better
performance. Extensive experiments on three commonly used fine-grained datasets
demonstrate that our approach is far superior to current state-of-the-art
web-supervised methods.

16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ