13 citations · 38 across the 9 of their papers we have counts for
6 papers
Weakly Supervised Object Localization Using Size Estimates
Miaojing Shi, Vittorio Ferrari
We present a technique for weakly supervised object localization (WSOL), building on the observation that WSOL algorithms usually work better on images with bigger objects. Instead…
Region-based semantic segmentation with end-to-end training
Holger Caesar, Jasper Uijlings, Vittorio Ferrari
We propose a novel method for semantic segmentation, the task of labeling each pixel in an image with a semantic class. Our method combines the advantages of the two main competing…
End-to-end training of object class detectors for mean average precision
Paul Henderson, Vittorio Ferrari
We present a method for training CNN-based object class detectors directly using mean average precision (mAP) as the training loss, in a truly end-to-end fashion that includes non-…
An active search strategy for efficient object class detection
Abel Gonzalez-Garcia, Alexander Vezhnevets, Vittorio Ferrari
Object class detectors typically apply a window classifier to all the windows in a large set, either in a sliding window manner or using object proposals. In this paper, we develop…
Articulated motion discovery using pairs of trajectories
Luca Del Pero, Susanna Ricco, Rahul Sukthankar +1
We propose an unsupervised approach for discovering characteristic motion patterns in videos of highly articulated objects performing natural, unscripted behaviors, such as tigers…
Closed-Form Training of Conditional Random Fields for Large Scale Image Segmentation
Alexander Kolesnikov, Matthieu Guillaumin, Vittorio Ferrari +1
We present LS-CRF, a new method for very efficient large-scale training of Conditional Random Fields (CRFs). It is inspired by existing closed-form expressions for the maximum like…