Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Grasp-Oriented Fine-grained Cloth Segmentation without Real Supervision   Automatically detecting graspable regions from a single depth image is a key
ingredient in cloth manipulation. The large variability of cloth deformations
has motivated most of the current approaches to focus on identifying specific
grasping points rather than semantic parts, as the appearance and depth
variations of local regions are smaller and easier to model than the larger
ones. However, tasks like cloth folding or assisted dressing require
recognising larger segments, such as semantic edges that carry more information
than points. The first goal of this paper is therefore to tackle the problem of
fine-grained region detection in deformed clothes using only a depth image. As
a proof of concept, we implement an approach for T-shirts, and define up to 6
semantic regions of varying extent, including edges on the neckline, sleeve
cuffs, and hem, plus top and bottom grasping points. We introduce a U-net based
network to segment and label these parts. The second contribution of our work
is concerned with the level of supervision that we require to train the
proposed network. While most approaches learn to detect grasping points by
combining real and synthetic annotations, in this work we defy the limitations
of the synthetic data, and propose a multilayered domain adaptation (DA)
strategy that does not use real annotations at all. We thoroughly evaluate our
approach on real depth images of a T-shirt annotated with fine-grained labels.
We show that training our network solely with synthetic data and the proposed
DA yields results competitive with models trained on real data.

2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con