Deep learning based instance segmentation of particle streaks and tufts

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...

Attention-based Deep Multiple Instance Learning

Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances. In this paper, we state the MIL problem as learning the Bernoulli distribution of the bag label where the bag label probability is fully parameterized by neural networks. Furthermore, we propose a neural network-based permutation-invariant aggregation operator tha...

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 ...

Multiple Instance Learning-Based Birdsong Classification Using Unsupervised Recording Segmentation

Traditional techniques for monitoring wildlife populations are temporally and spatially limited. Alternatively, in order to quickly and accurately extract information about the current state of the environment, tools for processing and recognition of acoustic signals can be used. In the past, a number of research studies on automatic classification of species through their vocalizations have be...