HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

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.
Document type :
Journal articles
Complete list of metadata

Cited literature [36 references]  Display  Hide  Download

Contributor : M. Pawan Kumar Connect in order to contact the contributor
Submitted on : Tuesday, November 3, 2015 - 5:27:13 PM
Last modification on : Friday, January 21, 2022 - 3:01:31 AM
Long-term archiving on: : Thursday, February 4, 2016 - 11:28:04 AM


Files produced by the author(s)



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⟩



Record views


Files downloads