BUILDING SECTION INSTANCE SEGMENTATION WITH COMBINED CLASSICAL AND DEEP LEARNING METHODS

Semantic Instance Segmentation via Deep Metric Learning

We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together. Our similarity metric is based on a deep, fully convolutional embedding model. Our grouping method is based on selecting all points that are sufficiently similar to a set of “seed points’, chosen from a deep, fully c...

Integrating Deep Learning and Multiple Instance Learning

Deep Multi-Instance Transfer Learning

We present a new approach for transferring knowledge from groups to individuals that comprise them. We evaluate our method in text, by inferring the ratings of individual sentences using full-review ratings. This approach combines ideas from transfer learning, deep learning and multi-instance learning, and reduces the need for laborious human labelling of fine-grained data when abundant labels ...

Instance-Level Segmentation with Deep Densely Connected MRFs

Our aim is to provide a pixel-wise instance-level labeling of a monocular image in the context of autonomous driving. We build on recent work [32] that trained a convolutional neural net to predict instance labeling in local image patches, extracted exhaustively in a stride from an image. A simple Markov random field model using several heuristics was then proposed in [32] to derive a globally ...

Semantic Segmentation with Deep Learning

We present a deep convolutional neural network approach for producing semantic segmentations. First, we generalize the architecture of the successful Alexnet network [7] to directly predict coarse segmentations. Second, we produce full resolution segmentations by re-ranking a diverse set of plausible segmentation proposals generated from a recent state of the art approach [9].