Optimizing Average Precision using Weakly Supervised Data

Abstract : Many tasks in computer vision, such as action classification and object detection, require us to rank a set of samples according to their relevance to a particular visual category. The performance of such tasks is often measured in terms of the average precision (AP). Yet it is common practice to employ the support vector machine (SVM) classifier, which optimizes a surrogate 0-1 loss. The popularity of SVM can be attributed to its empirical performance. Specifically, in fully supervised settings, SVM tends to provide similar accuracy to AP-SVM, which directly optimizes an AP-based loss. However, we hypothesize that in the significantly more challenging and practically useful setting of weakly supervised learning, it becomes crucial to optimize the right accuracy measure. In order to test this hypothesis, we propose a novel latent AP-SVM that minimizes a carefully designed upper bound on the AP-based loss function over weakly supervised samples. Using publicly available datasets, we demonstrate the advantage of our approach over standard loss-based learning frameworks on three challenging problems: action classification, character recognition and object detection.
Type de document :
Article dans une revue
IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2015, 37 (12), pp.2545-2557. 〈10.1109/TPAMI.2015.2414435 〉
Liste complète des métadonnées

Littérature citée [36 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01223977
Contributeur : M. Pawan Kumar <>
Soumis le : mardi 3 novembre 2015 - 17:27:13
Dernière modification le : vendredi 12 janvier 2018 - 11:17:34
Document(s) archivé(s) le : jeudi 4 février 2016 - 11:28:04

Fichier

BMJK-PAMI2015.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Aseem Behl, Pritish Mohapatra, C.V. Jawahar, M Pawan Kumar. Optimizing Average Precision using Weakly Supervised Data. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2015, 37 (12), pp.2545-2557. 〈10.1109/TPAMI.2015.2414435 〉. 〈hal-01223977〉

Partager

Métriques

Consultations de la notice

182

Téléchargements de fichiers

114