Conference paper Open Access

# Attention-enhanced Sensorimotor Object Recognition

Thermos, S; Papadopoulos, GT; Daras, P; Potamianos, G

### Citation Style Language JSON Export

{
"DOI": "10.1109/ICIP.2018.8451158",
"language": "eng",
"title": "Attention-enhanced Sensorimotor Object Recognition",
"issued": {
"date-parts": [
[
2018,
10,
10
]
]
},
"abstract": "<p>Sensorimotor learning, namely the process of understanding the physical world by combining visual and motor information, has been recently investigated, achieving promising results for the task of 2D/3D object recognition. Following the recent trend in computer vision, powerful deep neural networks (NNs) have been used to model the &ldquo;sensory&rdquo; and &ldquo;motor&rdquo; information, namely the object appearance and affordance. However, the existing implementations cannot efficiently address the spatio-temporal nature of the humanobject interaction. Inspired by recent work on attention-based learning, this paper introduces an attention-enhanced NN-based model that learns to selectively focus on parts of the physical interaction where the object appearance is corrupted by occlusions and deformations. The model&rsquo;s attention mechanism relies on the confidence of classifying an object based solely on its appearance. Three metrics are used to measure the latter, namely the prediction entropy, the average N-best likelihood difference, and the N-best likelihood dispersion. Evaluation of the attention-enhanced model on the SOR3D dataset reports 33% and 26% relative improvement over the appearance-only and the spatio-temporal fusion baseline models, respectively.</p>",
"author": [
{
"family": "Thermos, S"
},
{
},
{
"family": "Daras, P"
},
{
"family": "Potamianos, G"
}
],
"id": "3727849",
"version": "pre-print",
"type": "paper-conference",
"event": "IEEE ICIP 2018"
}
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