Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cross-modal Hallucination for Few-shot Fine-grained Recognition   State-of-the-art deep learning algorithms generally require large amounts of
data for model training. Lack thereof can severely deteriorate the performance,
particularly in scenarios with fine-grained boundaries between categories. To
this end, we propose a multimodal approach that facilitates bridging the
information gap by means of meaningful joint embeddings. Specifically, we
present a benchmark that is multimodal during training (i.e. images and texts)
and single-modal in testing time (i.e. images), with the associated task to
utilize multimodal data in base classes (with many samples), to learn explicit
visual classifiers for novel classes (with few samples). Next, we propose a
framework built upon the idea of cross-modal data hallucination. In this
regard, we introduce a discriminative text-conditional GAN for sample
generation with a simple self-paced strategy for sample selection. We show the
results of our proposed discriminative hallucinated method for 1-, 2-, and 5-
shot learning on the CUB dataset, where the accuracy is improved by employing
multimodal data.

14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math