Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FindIt: Generalized Localization with Natural Language Queries   We propose FindIt, a simple and versatile framework that unifies a variety of
visual grounding and localization tasks including referring expression
comprehension, text-based localization, and object detection. Key to our
architecture is an efficient multi-scale fusion module that unifies the
disparate localization requirements across the tasks. In addition, we discover
that a standard object detector is surprisingly effective in unifying these
tasks without a need for task-specific design, losses, or pre-computed
detections. Our end-to-end trainable framework responds flexibly and accurately
to a wide range of referring expression, localization or detection queries for
zero, one, or multiple objects. Jointly trained on these tasks, FindIt
outperforms the state of the art on both referring expression and text-based
localization, and shows competitive performance on object detection. Finally,
FindIt generalizes better to out-of-distribution data and novel categories
compared to strong single-task baselines. All of these are accomplished by a
single, unified and efficient model. The code will be released.

22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th