Deep Feature Fusion Multiple Instance Learning for WaDang Recognition

Deep Learning for Single-View Instance Recognition

Deep learning methods have typically been trained on large datasets in which many training examples are available. However, many real-world product datasets have only a small number of images available for each product. We explore the use of deep learning methods for recognizing object instances when we have only a single training example per class. We show that feedforward neural networks outp...

Multiple-Instance Learning: Multiple Feature Selection on Instance Representation

In multiple-Instance Learning (MIL), training class labels are attached to sets of bags composed of unlabeled instances, and the goal is to deal with classification of bags. Most previous MIL algorithms, which tackle classification problems, consider each instance as a represented feature. Although the algorithms work well in some prediction problems, considering diverse features to represent a...

Integrating Deep Learning and Multiple Instance Learning

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...

Multiple Instance Learning for Visual Recognition: Learning Latent Probabilistic Models

Many visual recognition tasks can be represented as multiple instance problems. Two examples are image categorization and video classification, where the instances are the image segments and video frames, respectively. In this regard, detecting and counting the instances of interest can help to improve recognition in a variety of applications. For example, classifying the collective activity of...