Essec\Faculty\Model\Contribution {#2233
#_index: "academ_contributions"
#_id: "13570"
#_source: array:26 [
"id" => "13570"
"slug" => "diablo-dictionary-based-attention-block-for-deep-metric-learning"
"yearMonth" => "2020-07"
"year" => "2020"
"title" => "DIABLO: Dictionary-based attention block for deep metric learning"
"description" => "JACOB, P., PICARD, D., HISTACE, A. et KLEIN, E. (2020). DIABLO: Dictionary-based attention block for deep metric learning. <i>Pattern Recognition Letters</i>, 135, pp. 99-105."
"authors" => array:5 [
0 => array:3 [
"name" => "JACOB Pierre"
"bid" => "B00795650"
"slug" => "jacob-pierre"
]
1 => array:1 [
"bid" => "B00778976"
]
2 => array:1 [
"name" => "PICARD David"
]
3 => array:1 [
"name" => "HISTACE Aymeric"
]
4 => array:1 [
"name" => "KLEIN Edouard"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2024-08-22 14:23:10"
"publicationUrl" => "https://doi.org/10.1016/j.patrec.2020.03.020"
"publicationInfo" => array:3 [
"pages" => "99-105"
"volume" => "135"
"number" => ""
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "Recent breakthroughs in representation learning of unseen classes and examples have been made in deep metric learning by training at the same time the image representations and a corresponding metric with deep networks. Recent contributions mostly address the training part (loss functions, sampling strategies, etc.), while a few works focus on improving the discriminative power of the image representation. In this paper, we propose DIABLO, a dictionary-based attention method for image embedding. DIABLO produces richer representations by aggregating only visually-related features together while being easier to train than other attention-based methods in deep metric learning. This is experimentally confirmed on four deep metric learning datasets (Cub-200-2011, Cars-196, Stanford Online Products, and In-Shop Clothes Retrieval) for which DIABLO shows state-of-the-art performances."
"en" => "Recent breakthroughs in representation learning of unseen classes and examples have been made in deep metric learning by training at the same time the image representations and a corresponding metric with deep networks. Recent contributions mostly address the training part (loss functions, sampling strategies, etc.), while a few works focus on improving the discriminative power of the image representation. In this paper, we propose DIABLO, a dictionary-based attention method for image embedding. DIABLO produces richer representations by aggregating only visually-related features together while being easier to train than other attention-based methods in deep metric learning. This is experimentally confirmed on four deep metric learning datasets (Cub-200-2011, Cars-196, Stanford Online Products, and In-Shop Clothes Retrieval) for which DIABLO shows state-of-the-art performances."
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2024-12-22T08:21:46.000Z"
"docTitle" => "DIABLO: Dictionary-based attention block for deep metric learning"
"docSurtitle" => "Articles"
"authorNames" => "<a href="/cv/jacob-pierre">JACOB Pierre</a>, PICARD David, HISTACE Aymeric, KLEIN Edouard"
"docDescription" => "<span class="document-property-authors">JACOB Pierre, PICARD David, HISTACE Aymeric, KLEIN Edouard</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2020</span>"
"keywordList" => ""
"docPreview" => "<b>DIABLO: Dictionary-based attention block for deep metric learning</b><br><span>2020-07 | Articles </span>"
"docType" => "research"
"publicationLink" => "<a href="https://doi.org/10.1016/j.patrec.2020.03.020" target="_blank">DIABLO: Dictionary-based attention block for deep metric learning</a>"
]
+lang: "fr"
+"_type": "_doc"
+"_score": 8.831362
+"parent": null
}