Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rethinking movie genre classification with fine-grained semantic
  clustering   Movie genre classification is an active research area in machine learning.
However, due to the limited labels available, there can be large semantic
variations between movies within a single genre definition. We expand these
'coarse' genre labels by identifying 'fine-grained' semantic information within
the multi-modal content of movies. By leveraging pre-trained 'expert' networks,
we learn the influence of different combinations of modes for multi-label genre
classification. Using a contrastive loss, we continue to fine-tune this
'coarse' genre classification network to identify high-level intertextual
similarities between the movies across all genre labels. This leads to a more
'fine-grained' and detailed clustering, based on semantic similarities while
still retaining some genre information. Our approach is demonstrated on a newly
introduced multi-modal 37,866,450 frame, 8,800 movie trailer dataset,
MMX-Trailer-20, which includes pre-computed audio, location, motion, and image
embeddings.

34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex