Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Trans4Trans: Efficient Transformer for Transparent Object and Semantic
  Scene Segmentation in Real-World Navigation Assistance   Transparent objects, such as glass walls and doors, constitute architectural
obstacles hindering the mobility of people with low vision or blindness. For
instance, the open space behind glass doors is inaccessible, unless it is
correctly perceived and interacted with. However, traditional assistive
technologies rarely cover the segmentation of these safety-critical transparent
objects. In this paper, we build a wearable system with a novel dual-head
Transformer for Transparency (Trans4Trans) perception model, which can segment
general- and transparent objects. The two dense segmentation results are
further combined with depth information in the system to help users navigate
safely and assist them to negotiate transparent obstacles. We propose a
lightweight Transformer Parsing Module (TPM) to perform multi-scale feature
interpretation in the transformer-based decoder. Benefiting from TPM, the
double decoders can perform joint learning from corresponding datasets to
pursue robustness, meanwhile maintain efficiency on a portable GPU, with
negligible calculation increase. The entire Trans4Trans model is constructed in
a symmetrical encoder-decoder architecture, which outperforms state-of-the-art
methods on the test sets of Stanford2D3D and Trans10K-v2 datasets, obtaining
mIoU of 45.13% and 75.14%, respectively. Through a user study and various
pre-tests conducted in indoor and outdoor scenes, the usability and reliability
of our assistive system have been extensively verified. Meanwhile, the
Tran4Trans model has outstanding performances on driving scene datasets. On
Cityscapes, ACDC, and DADA-seg datasets corresponding to common environments,
adverse weather, and traffic accident scenarios, mIoU scores of 81.5%, 76.3%,
and 39.2% are obtained, demonstrating its high efficiency and robustness for
real-world transportation applications.

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The categories of this paper are: math
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
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The categories of this paper are: physics
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ex