429 citations · 880 across the 60 of their papers we have counts for
7 papers · 2 filters
COCO-Stuff: Thing and Stuff Classes in Context
Holger Caesar, Jasper Uijlings, Vittorio Ferrari
Semantic classes can be either things (objects with a well-defined shape, e.g. car, person) or stuff (amorphous background regions, e.g. grass, sky). While lots of classification a…
Learning Semantic Part-Based Models from Google Images
Davide Modolo, Vittorio Ferrari
We propose a technique to train semantic part-based models of object classes from Google Images. Our models encompass the appearance of parts and their spatial arrangement on the o…
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…
Do semantic parts emerge in Convolutional Neural Networks?
Abel Gonzalez-Garcia, Davide Modolo, Vittorio Ferrari
Semantic object parts can be useful for several visual recognition tasks. Lately, these tasks have been addressed using Convolutional Neural Networks (CNN), achieving outstanding r…
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-…