Integrating Structural Information in Deep Convolutional Neural
Networks for Low- and High-Level Vision
(Ecole Centrale Paris, France)
Deep Convolutional Neural Networks (DCNNs) have demonstrated excellent
performance in a large variety of low- and high-level tasks, but they are
largely considered as `black box' classifiers that do not exploit the insights
we have about the computer vision task. In this talk we will present recent
efforts on incorporating classical computer vision ideas in DCNNs used to
addressed both low- and high- level tasks.
We will start with scale-invariance
in image labelling and image classification, move on to treating scale- and
aspect- variation in object detection, and finally turn to fully-connected
models for semantic image segmentation and boundary detection. The common theme
in all these works is the use of fully convolutional neural networks, aimed at
substituting the commonly hand-engineered pipelies as generic visual front-ends.
Our semantic segmentation system is publicly available from this link: