Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: All You Can Embed: Natural Language based Vehicle Retrieval with
  Spatio-Temporal Transformers   Combining Natural Language with Vision represents a unique and interesting
challenge in the domain of Artificial Intelligence. The AI City Challenge Track
5 for Natural Language-Based Vehicle Retrieval focuses on the problem of
combining visual and textual information, applied to a smart-city use case. In
this paper, we present All You Can Embed (AYCE), a modular solution to
correlate single-vehicle tracking sequences with natural language. The main
building blocks of the proposed architecture are (i) BERT to provide an
embedding of the textual descriptions, (ii) a convolutional backbone along with
a Transformer model to embed the visual information. For the training of the
retrieval model, a variation of the Triplet Margin Loss is proposed to learn a
distance measure between the visual and language embeddings. The code is
publicly available at https://github.com/cscribano/AYCE_2021.

35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys