Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multimodal Representation Learning With Text and Images   In recent years, multimodal AI has seen an upward trend as researchers are
integrating data of different types such as text, images, speech into modelling
to get the best results. This project leverages multimodal AI and matrix
factorization techniques for representation learning, on text and image data
simultaneously, thereby employing the widely used techniques of Natural
Language Processing (NLP) and Computer Vision. The learnt representations are
evaluated using downstream classification and regression tasks. The methodology
adopted can be extended beyond the scope of this project as it uses
Auto-Encoders for unsupervised representation learning.

35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math