Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Joint Supervised and Self-Supervised Learning for 3D Real-World
  Challenges   Point cloud processing and 3D shape understanding are very challenging tasks
for which deep learning techniques have demonstrated great potentials. Still
further progresses are essential to allow artificial intelligent agents to
interact with the real world, where the amount of annotated data may be limited
and integrating new sources of knowledge becomes crucial to support autonomous
learning. Here we consider several possible scenarios involving synthetic and
real-world point clouds where supervised learning fails due to data scarcity
and large domain gaps. We propose to enrich standard feature representations by
leveraging self-supervision through a multi-task model that can solve a 3D
puzzle while learning the main task of shape classification or part
segmentation. An extensive analysis investigating few-shot, transfer learning
and cross-domain settings shows the effectiveness of our approach with
state-of-the-art results for 3D shape classification and part segmentation.

36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an