Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Egocentric Human-Object Interaction Detection Exploiting Synthetic Data   We consider the problem of detecting Egocentric HumanObject Interactions
(EHOIs) in industrial contexts. Since collecting and labeling large amounts of
real images is challenging, we propose a pipeline and a tool to generate
photo-realistic synthetic First Person Vision (FPV) images automatically
labeled for EHOI detection in a specific industrial scenario. To tackle the
problem of EHOI detection, we propose a method that detects the hands, the
objects in the scene, and determines which objects are currently involved in an
interaction. We compare the performance of our method with a set of
state-of-the-art baselines. Results show that using a synthetic dataset
improves the performance of an EHOI detection system, especially when few real
data are available. To encourage research on this topic, we publicly release
the proposed dataset at the following url:
https://iplab.dmi.unict.it/EHOI_SYNTH/.

13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph