Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Move to See Better: Self-Improving Embodied Object Detection   Passive methods for object detection and segmentation treat images of the
same scene as individual samples and do not exploit object permanence across
multiple views. Generalization to novel or difficult viewpoints thus requires
additional training with lots of annotations. In contrast, humans often
recognize objects by simply moving around, to get more informative viewpoints.
In this paper, we propose a method for improving object detection in testing
environments, assuming nothing but an embodied agent with a pre-trained 2D
object detector. Our agent collects multi-view data, generates 2D and 3D
pseudo-labels, and fine-tunes its detector in a self-supervised manner.
Experiments on both indoor and outdoor datasets show that (1) our method
obtains high-quality 2D and 3D pseudo-labels from multi-view RGB-D data; (2)
fine-tuning with these pseudo-labels improves the 2D detector significantly in
the test environment; (3) training a 3D detector with our pseudo-labels
outperforms a prior self-supervised method by a large margin; (4) given weak
supervision, our method can generate better pseudo-labels for novel objects.

9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat