Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Weakly supervised cross-domain alignment with optimal transport   Cross-domain alignment between image objects and text sequences is key to
many visual-language tasks, and it poses a fundamental challenge to both
computer vision and natural language processing. This paper investigates a
novel approach for the identification and optimization of fine-grained semantic
similarities between image and text entities, under a weakly-supervised setup,
improving performance over state-of-the-art solutions. Our method builds upon
recent advances in optimal transport (OT) to resolve the cross-domain matching
problem in a principled manner. Formulated as a drop-in regularizer, the
proposed OT solution can be efficiently computed and used in combination with
other existing approaches. We present empirical evidence to demonstrate the
effectiveness of our approach, showing how it enables simpler model
architectures to outperform or be comparable with more sophisticated designs on
a range of vision-language tasks.

12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph