Open AccessProceedings Article
Do Convnets Learn Correspondence
Jonathan Long,Ning Zhang,Trevor Darrell +2 more
- 08 Dec 2014
- Vol. 27, pp 1601-1609
TL;DR: In this paper, the effectiveness of convnet activation features for tasks requiring correspondence was studied and shown that convnet features localize at a much finer scale than their receptive field sizes, that they can be used to perform intraclass aligment as well as conventional hand-engineered features.
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Abstract: Convolutional neural nets (convnets) trained from massive labeled datasets [1] have substantially improved the state-of-the-art in image classification [2] and object detection [3]. However, visual understanding requires establishing correspondence on a finer level than object category. Given their large pooling regions and training from whole-image labels, it is not clear that convnets derive their success from an accurate correspondence model which could be used for precise localization. In this paper, we study the effectiveness of convnet activation features for tasks requiring correspondence. We present evidence that convnet features localize at a much finer scale than their receptive field sizes, that they can be used to perform intraclass aligment as well as conventional hand-engineered features, and that they outperform conventional features in keypoint prediction on objects from PASCAL VOC 2011 [4].
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