Optimizing Average Precision using Weakly Supervised Data.

Aseem Behl (homepage)

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. (more...)

Related Publications

• Aseem Behl, Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar - Optimizing Average Precision using Weakly Supervised Data IEEE Transations on Pattern Analysis and Machine Intelligence (TPAMI 2015). [PDF]

• Puneet K. Dokania, Aseem Behl, C.V. Jawahar, M. Pawan Kumar -  Learning to Rank using High-Order Information Proceedings of European Conference on Computer Vision,06-12 Sep 2014, Zurich. [PDF]
  

• Aseem Behl, C.V. Jawahar and M. Pawan Kumar - Optimizing Average Precision using Weakly Supervised Data Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 23-28 June 2014, Columbus, Ohio, USA. [PDF]

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