Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification   Existing computer vision research in artwork struggles with artwork's
fine-grained attributes recognition and lack of curated annotated datasets due
to their costly creation. To the best of our knowledge, we are one of the first
methods to use CLIP (Contrastive Language-Image Pre-Training) to train a neural
network on a variety of artwork images and text descriptions pairs. CLIP is
able to learn directly from free-form art descriptions, or, if available,
curated fine-grained labels. Model's zero-shot capability allows predicting
accurate natural language description for a given image, without directly
optimizing for the task. Our approach aims to solve 2 challenges: instance
retrieval and fine-grained artwork attribute recognition. We use the iMet
Dataset, which we consider the largest annotated artwork dataset. In this
benchmark we achieved competitive results using only self-supervision.

5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th